Data Analytics

UKG unlocks real-time workforce intelligence at scale with the Agentic Data Cloud

Wed, 29 Apr 2026 16:00:00 +0000

At UKG, we’ve spent years building and expanding our human capital management (HCM) and workforce management (WFM) solutions with new products, capabilities, and a series of acquisitions. Our cloud platform includes a suite of connected systems that support every corner of the employee experience, including scheduling and workforce operations, HR and payroll, and culture and engagement tools.

These connected tools offer customers incredible depth, but it also means our backend reflects years of evolution. We have 126 application teams, dozens of tech stacks, and more than 12,000 database instances inherited through acquisitions and product growth. And each product carries its own schema and operational footprint.

Previously, data moved through bespoke pipelines not built for real-time use. As AI advanced, expectations did too. Customers wanted instant insights across HR, time, pay, culture, and operations, and those insights increasingly needed to drive automated workflows and intelligent applications.

Internally, teams needed consistent, high-performance access to shared data to innovate faster and modernize our architecture. We needed a unified foundation for the next generation of intelligence across our suite. That’s why we built People Fabric, our new data and intelligence platform powered by AlloyDB for PostgreSQL and the just-announced Agentic Data Cloud.

Unifying the systems behind the suite

People Fabric started with a simple need: bring the full UKG suite onto one real-time foundation. Getting there started with defining a single canonical data model for the entire suite. This would serve as the shared language for people, work, pay, and culture data — consistent no matter where the information originated.

We needed an operational database that could ingest changes quickly and scale horizontally. That’s why we chose AlloyDB as the core of People Fabric. It gives us millisecond-level read-after-write behavior, high-throughput ingestion, scalable read pools, and native vector capabilities to support AI.

With the model defined and the operational store selected, the next step was building the pipeline that feeds the platform. We created a custom change data capture (CDC) framework to extract changes from our existing operational databases inherited over the years. Those changes flow through Dataflow, where they’re transformed into the canonical structure that AlloyDB for PostgreSQL expects.

Once in AlloyDB, that data becomes the real-time backbone of the platform. Applications use it for near-instant queries. AI agents rely on it for cross-domain decisions, and vector search engines use it to power natural-language and similarity-based experience layers.

For larger analytical workloads, the same data flows into BigQuery, which gives our teams and our customers the ability to perform organization-wide reporting and analysis without straining the system. Cloud SQL holds the metadata and tenancy context that govern who can see what and how different parts of the suite interact with People Fabric. From there, the system runs continuously. Data enters through streaming ingestion and gets modeled once in AlloyDB for PostgreSQL to make it available everywhere.

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Bringing people intelligence to intelligent people

With the architecture in place, People Fabric gives us something we never had before: a complete and consistent view of people, work, pay, and culture data that’s updated continuously and ready for AI to use in real time.

That unified context is what powers our assistive experiences, including conversational reporting and natural-language interactions. Leaders can ask questions in plain English and get answers that reflect the full picture — not just a single system’s slice of it.

With the power of Google’s Agentic Data Cloud, our platform unifies analytical and transactional data to power real-time AI. This allows agents to reason over live workforce signals and trigger immediate actions. Because this data is governed and modeled from the start, our agents can reliably handle multi-step workflows across HR, payroll, and timekeeping.

Whether they're identifying pay discrepancies, adjusting schedules, or flagging compliance risks, they operate with the same shared semantics and security model that guides our applications. It’s the difference between AI that reacts and AI that can truly assist.

Driving impact across every layer

For engineering teams, People Fabric acts as a database-as-a-service that removes the need for each microservice to manage its own datastore or pipelines. This accelerates development and supports modernization without customer disruption.

AlloyDB for PostgreSQL delivers millisecond read-after-write behavior, zero replication lag, and near-real time ingestion latency, enabling real-time workloads with far less complexity. Migrating core person and employment data off our on-prem monolith has generated cost savings significant enough to fund half of People Fabric.

Real-time operational data now gives managers a live view of staffing, pay, and workforce activity. More than 1,000 organizations are already on the platform, with another 1,000 in progress.

As we continue expanding People Fabric, we’re laying the groundwork for deeper agentic automation, more responsive analytics, and a growing set of AI-driven capabilities — all on a trusted, scalable foundation built for what’s next.

Learn more

UKG’s success illustrates how leveraging AlloyDB for PostgreSQL and the Agentic Data Cloud allows organizations to unify operational and analytical data, creating the essential foundation for real-time, agentic AI.

Learn more about AlloyDB for PostgreSQL and get started with a free trial today!
Sign up for a strategy workshop today on how to get your data ready for the agentic era!

Mapping a smarter future with BigQuery and Google Earth AI models and datasets

Mon, 27 Apr 2026 16:00:00 +0000

Last year we introduced new geospatial analytics capabilities integrated for BigQuery. Building on this, we announced an expanded suite of tools at Google Cloud Next ‘26, designed to help your business unlock deeper insights and make smarter, data-driven decisions. These Google Maps Platform models and datasets, leveraging innovation from Google Earth AI, are integrated with BigQuery and Gemini Enterprise Agent Platform. They help you transform geospatial information into actionable intelligence, empowering you to understand our planet and its communities like never before.

Harnessing AI for planetary understanding

In March we launched Street View Insights in general availability, which draws on Google Street View’s vast repository of over 280 billion images and turns them into actionable understanding of physical infrastructure. This enables customers in telecom, utilities and the public sector to reduce weeks of manual work to minutes and get insights right from their desks. In the coming weeks we’re bringing the experimental release of LiDAR data to Street View Insights, providing precise measurements of infrastructure. With this, you can accurately determine the height of utility poles, the clearance of overhead lines, or the specific dimensions of road signs without having to manually gather measurements from the field.

We’re also expanding our Imagery portfolio in the coming weeks to include the experimental release of Aerial and Satellite Insights, providing a multi-perspective view of infrastructure that includes aerial, satellite and Street View imagery. This will help organizations manage assets at scale and with context. You can now combine top-down aerial and satellite views for large-scale planning and regional assessments with the ground-level detail of Street View to verify specific asset conditions.

Finally, we’re taking geospatial analysis to new heights with our Aerial and Satellite Models, developed as part of Google Research’s Remote Sensing Foundation effort, and now available in experimental within Model Garden. Now you can license our stand-alone model to build custom applications on any high-resolution aerial or satellite imagery source. Read our blog to learn more about Street View Insights, Aerial and Satellite Insights, and Aerial and Satellite Models.

With Aerial and Satellite Models, an energy analyst can type a prompt like “find large HVAC cooling towers”. The model identifies relevant cooling tower objects across large geographies.

How Vantor is using Aerial and Satellite Models

Following a severe storm, recovery teams need a clear picture of the damage to help communities rebuild. Vantor, a leading spatial intelligence company, uses these models in its Sentry application to turn raw satellite imagery into actionable insights. This helps organizations quickly identify washed-out roads and damaged infrastructure, so they can proactively remove storm debris and prioritize long-term repairs.

“The combination of Vantor’s spatial foundation and Google’s Aerial and Satellite Models is creating a new class of geospatial intelligence systems that can interpret activity across the planet, surface meaningful signals, and deliver insights directly into operational workflows. In demonstrations with customers, where we’ve integrated models into our persistent monitoring application called Sentry, the level of insight has been remarkable.” - Peter Wilczynski, Chief Product Officer, Vantor

Vantor’s Sentry application uses Aerial and Satellite Models to turn raw imagery into actionable insights. After a storm, this helps their own users quickly identify washed-out roads and damaged infrastructure, so they can proactively remove storm debris and prioritize long-term repairs.

Understanding communities

To learn about populations and their behaviors, researchers typically rely on three types of data sources — censuses, surveys, and satellite imagery — all of which are infrequently updated and can lack scale.

To address this, we’re announcing the preview of Population Dynamics Insights, a first-of-its-kind geospatial embeddings dataset powered by Google Research’s Population Dynamics Foundation Model (PDFM) designed to help organizations decode the complex relationship between human behavior and the physical world. By distilling anonymized trends derived from Google search trends, Google Maps points of interest, busyness, air quality and pollen data into rich 330-dimensional vectors for places across the globe, it enables a new era of spatial machine learning without the need for manual feature engineering. Learn more in our blog.

Safer and smarter road networks

We want to help local authorities make roads safer and smoother for everyone. That’s why we’re adding new preview features to Road Management Insights. You can now measure vehicle counts, to provide accurate traffic estimates that are required to evaluate the impact of new roads, bridges, and major maintenance projects. We’re also adding real-time disruptions for things like road closures that provide early signals about the potential reasons for traffic slowdowns. Finally, we’re announcing that Road Management Insights is expanding beyond the public sector, and is now available to logistics and roadside assistance companies. Get more information in our blog.

Accelerate renewable energy adoption

We’re also introducing the experimental release of Solar Insights, now available in BigQuery. Built on the same imagery data and models available within Aerial and Satellite Insights and Aerial and Satellite Models, it provides high-resolution, building-level data on solar potential and existing arrays to help utilities and service providers accelerate renewable-energy adoption and optimize network planning. With Solar Insights, you can predict the next frontier of renewable energy market opportunities with BigQuery. Overlay information about solar potential per building, along with existing solar deployments to reveal untapped market opportunities and optimize investment strategies. Additionally, integrating these building-level details with our weather models and historical weather data allows you to accurately predict rooftop solar power contributions, increasing energy reliability and driving more profitable investments in renewable infrastructure. Learn more about Solar Insights here.

Optimize health and well-being

Understanding how environmental factors impact health is more crucial than ever. We're excited to announce new environment datasets, now available in experimental through Google Maps Platform. These datasets provide air quality, pollen and weather insights, and enable you to go beyond real-time data to unlock environmental understanding through hyper-local, high-resolution historical data that’s tightly integrated with BigQuery. This makes it easy to spot long-term patterns, like how allergy seasons affect your business or where air quality impacts public health. By mixing this environmental data with your own records, you can stop reacting to the weather and start planning for it. Whether you're deciding where to send resources or how to protect your customers, you’ll have the full picture of how the environment shapes your world. Read more in our blog.

Visualizing the median PM2.5 levels in Manhattan on a specific day

How these datasets can work together

One example of how these datasets can work together is Google for Health's Population Health AI (PHAI), an advanced analytics engine that helps identify hidden health risks within communities. The goal is to equip our partners with insights that could help them shift from treating problems to proactively managing chronic condition risks. To provide this comprehensive view, PHAI utilizes Google Maps Platform’s Population Dynamics Insights, Places Insights and air quality and pollen datasets. By analyzing these diverse, de-identified data sets — ranging from geographic factors like the air we breathe to local access to fresh food — the AI model helps healthcare providers understand the shift from reactive treatment to proactive, tailored management of chronic condition risks for specific towns or postcodes.

Ready to explore what's possible? Visit our website to discover how Google's geospatial analytics can help you unlock your next big opportunity, or sign up for early access and to learn more.

What’s new with Google Data Cloud

Thu, 23 Apr 2026 16:00:00 +0000

April 20 - April 24

Google-built ODBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built ODBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your application to BigQuery.

April 13 - April 17

We announced we are reintroducing Data Studio to play a significant role in the AI era, expanding from data visualizations and reports to host BigQuery conversational agents and data apps built in Colab notebooks.
We announced BigQuery Graph is now available in preview, offering an easy-to-use, highly scalable graph analytics solution, empowering data professionals to model, analyze and visualize massive-scale relationships in an entirely new way.

April 6 - April 10

We introduced Conversational Analytics for Looker Embedded environments, enabling users to add natural language experiences to their own custom data-driven applications, powered by Gemini.
We expanded Looker’s capabilities for faster ad-hoc analysis, with the introduction of self-service Explores, enabling you to bring your own data to Looker’s semantic layer and gain instant access to insights in a governed data environment.

March 23 - March 27

We showed you how you can scale your reads with Cloud SQL autoscaling read pools. This feature allows you to provision multiple read replicas that are accessible via a single read endpoint and to dynamically adjust your read capability based on real-time application needs.
Our customers are leveraging the full power of Conversational Analytics and Looker to drive major business and technical breakthroughs in the AI era. Companies like Telenor, Pet Circle, Fluent Commerce, Lighthouse Intelligence, Wego, and ROLLER are turning data into insights and actions, grounded by Looker’s semantic layer.

March 16 - March 20

We introduced an enhanced Gemini assistant in BigQuery Studio, transforming the agent from a code assistant into a fully context-aware analytics partner.

February 23 - February 27

We introduced managed and remote MCP support for Google Cloud databases, including AlloyDB, Spanner, Cloud SQL, Bigtable and Firestore, to power the next generation of agents. This announcement extends the ability for AI models to plan, build, and solve complex problems, connecting to the database tools our customers leverage daily as the backbone of their work environment.
We outlined how you can build a conversational agent in BigQuery using the Conversational Analytics API to help you build context-aware agents that can understand natural language, query your BigQuery data, and deliver answers in text, tables, and visual charts.

February 16 - February 20

Our customers are leveraging the full power of Looker to drive major business and technical breakthroughs. Companies like Arrive, Audika, Carousell, Framebridge, GumGum, Intel, Overdose Digital, Ocean Network Express, Subskribe and Promevo are leveraging Looker’s newest AI-driven capabilities, including Conversational Analytics, to transform data to insights and actions, and empower their entire organization with a single source of truth, powered by Looker’s semantic layer.

February 2 - February 6

Join us on March 4 for our webinar, Win Your AI Strategy with Cloud SQL Enterprise Plus, to learn how to power your generative AI workloads with 3x higher performance and 99.99% availability. Register today to discover how to build a scalable, enterprise-grade foundation for your most demanding AI applications.

January 26 - January 30

We introduced Conversational Analytics in BigQuery, which allows users to analyze data using natural language. Conversational Analytics in BigQuery is an intelligent agent that generates, executes and visualizes answers grounded in your business context directly in BigQuery Studio, making data insights for data professionals more conversational.
We outlined how data products have become the foundation for AI agents, providing the context needed to make autonomous agents reliable and trusted for real business use, backed by organized business logic and semantic understanding.
We highlighted how you can supercharge data analytics workflows, and outlined Google Cloud’s AI agent offerings for data engineering, data science, and development tools, so you can integrate agentic workflows in your applications, empower your teams and speed discovery.

January 19 - January 23

We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.
Introducing Google Cloud SQL on MSSQLTips: We are highlighting a new technical guide published on MSSQLTips titled "Introducing Google Cloud SQL." This article serves as an essential resource for SQL Server administrators and developers exploring Google Cloud's fully managed database service. It provides a detailed overview of Cloud SQL capabilities, including high availability, security integration, and the seamless transition of on-premises SQL Server workloads to the cloud, making it an ideal resource for those planning their migration strategy.
We are excited to announce the Public Preview of Microsoft Entra ID (formerly Azure Active Directory) integration with Cloud SQL for SQL Server. Designed to tackle the challenge of identity sprawl in multi-cloud environments, this integration allows organizations to govern database access using their existing Microsoft identity infrastructure. Key benefits include centralized identity management, enhanced security features like Multi-Factor Authentication (MFA), and simplified user administration through direct group mapping. This feature is available for SQL Server 2022 and supports both public and private IP configurations.

January 12 - January 16

Google-built JDBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built JDBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for Java applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your Java application to BigQuery.
Troubleshoot Airflow tasks instantly with Gemini Cloud Assist investigations: Cloud Composer just got smarter. We are excited to announce that Gemini Cloud Assist investigations are now available directly within Cloud Composer 3. Instead of manually sifting through raw logs, you can now simply click "Investigate" on a failed Airflow task. Gemini analyzes logs and task metadata to identify failure patterns—such as resource exhaustion or timeouts—and provides actionable recommendations driven by Gemini Cloud Assist to resolve the issue. This integration shifts the debugging experience from manual toil to automated root cause analysis, significantly reducing the time required to restore your pipelines. Learn more about AI-assisted troubleshooting.

Day 1 at Google Cloud Next ‘26 recap

Wed, 22 Apr 2026 23:00:00 +0000

Last year at Google Cloud Next ‘25, we asked you to imagine a new future for AI. At Next ‘26, the question before you is how do you move AI into production across your entire enterprise?

According to Google Cloud CEO Thomas Kurian, the answer is straightforward: You need a unified stack, with “chips that are designed for models, models that are grounded in your data, agents and applications that are built with those models,” and the whole thing “secured by the infrastructure,” Thomas said in his keynote. (This is the same unified stack that Google uses for Search, YouTube, Chrome, and Android. As Alphabet CEO Sundar Pichai said in his opening remarks, “a big focus of ours is to always be customer zero for our own technologies.”)

As AI matures, we’ve laid out a blueprint on how to succeed. Read on for a whirlwind tour of what we announced from the keynote stage.

Gemini Enterprise: The end-to-end system for the agentic era

Throughout this unified stack is Gemini Enterprise — “the connective tissue between your data, your people, and your goals,” Thomas said, providing a combination of intelligence and automation across multiple layers. Here’s what’s new.

1. Gemini Enterprise Agent Platform

Gemini Enterprise Agent Platform is where you go to build, scale, govern, and optimize agents. As the evolution of Vertex AI, it’s built on top of our leading infrastructure, and deeply integrated with our data and security capabilities — the foundation of the Agentic Enterprise. Here’s a sampling of Agent Platform’s new features and capabilities:

Build: Choose the right environment for the job — from the low-code, visual interface of the new Agent Studio, to the code-first logic of the upgraded Agent Development Kit (ADK). We’ve simplified the entire lifecycle with AI-native coding capabilities to help you ship production-grade agents faster.
Scale: Clear the path to production with the re-engineered Agent Runtime. This supports long-running agents that maintain state for days at a time and are backed by Memory Bank for persistent, long-term context.
Govern: Establish centralized control with Agent Identity, Agent Registry, and Agent Gateway. These capabilities help ensure every agent — whether built on Agent Platform or sourced from our partner ecosystem — has a trackable identity and operates within enterprise-grade guardrails.
Optimize: Guarantee quality with Agent Simulation, Agent Evaluation, and Agent Observability. These tools provide full execution traces and a real-time lens into agent reasoning to help ensure your agents always hit their goals.

To dive deep into Agent Platform, read more in our announcement blog here.

2. Gemini Enterprise app

The Gemini Enterprise app is “the primary environment where your business actually operates,” Thomas explained. The app is where many workers, especially non-technical ones, can ask questions of enterprise agents, create generative media, engage with prebuilt agents, and even create their own with conversational interfaces — all with governance, compliance, and security built in. Here’s a sample of what’s new in this foundational interface:

Gemini Enterprise Projects give your agents permanent memory.
Deep Think solves your most complex business challenges without context pollution.
Microsoft 365 interoperability makes it easy to export docs you create with Canvas into Microsoft Office formats.

To illustrate the power of Gemini Enterprise, Shaun White, three-time Olympic gold medalist, entrepreneur, and snowboarding legend, joined us on stage.

“Back when I was training, our tools were camcorders and guesswork. You’d land a trick and watch it back. And you’d be thinking, ‘How can I make that trick better?’” he said.

Alongside Jason Davenport, Google Cloud Tech Lead, they showed a model that Google Cloud built in collaboration with Google DeepMind that tracked Shaun in space from a two-dimensional video, helping him understand what he was doing right and wrong.

“Learning the trick on the mountain is one thing, but actually understanding the physics of a trick is a whole other thing,” Shaun said.

Read more on the Gemini Enterprise app here.

3. AI Hypercomputer

The same technology foundation that athletes like Shaun White use to understand their performance is being used by enterprises to transform their businesses.

Amin Vadhat, SVP and chief technologist, AI and Infrastructure, took to the stage to announce enhancements to AI Hypercomputer, the integrated supercomputing underneath every AI workload on Google Cloud.

First, there’s the eighth-generation Tensor Processing Unit, or TPU — “a thing of beauty,” Amin said. And because “the demands of training and serving have completely diverged,” this TPU family actually consists of two chips:

TPU 8t, optimized for training, uses new Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 petabytes of shared, high-bandwidth memory in a single superpod. It achieves three times the processing power of Ironwood and delivers up to double the performance per watt.
TPU 8i, optimized for inference, uses the new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features three times more on-chip SRAM compared to previous versions and a specialized Collectives Acceleration Engine offloads resource-heavy tasks. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, helping millions of concurrent agents to run cost-effectively.

AI Hypercomputer also supports Arm-based Google Cloud Axion processors, such as the N4A, now generally available, and will be one of the first platforms to offer NVIDIA’s Vera Rubin NVL72 platform when it is released.

Other parts of the network need to keep up with the demands of the agentic era. For example, the new Virgo Network doubles connectivity to scale training beyond AI Hypercomputer superpods, and Google Cloud Managed Lustre now supports an industry-leading 10 terabytes per second of throughput.

Learn more about all of our AI infrastructure innovations here.

4. Agentic Data Cloud

The AI era hinges on data. Lots of data. That data comes with a catch: It needs to be grounded in context. That’s because “reasoning without context is just a guess,” explained Karthik Narain, chief product and business officer.

To that end, we’re totally rethinking our data platform, and giving it a new name: the Agentic Data Cloud.

Here’s a sampling of what you’ll find in the Agentic Data Cloud:

Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Knowledge Catalog is also integrated with Gemini’s Deep Research Agent.
Data Agent Kit delivers a Gemini-powered data science authoring experience across your IDEs, Notebooks, and agentic terminals.
Lightning Engine for Apache Spark is a real-time, serverless engine that is up to 4.5 times faster than open-source alternatives and offers up to double price-performance over the leading competitor for large datasets.
Finally, Cross-Cloud Lakehouse, based on Apache Iceberg, lets you query data in Amazon Web Services or Azure without having to copy it.

Learn all about all the innovations in the Agentic Data Cloud here.

5. Agentic Defense

AI makes — and demands — that everything go faster, and security operations are no exception. Increasingly, “human analysts can’t keep up with AI-driven attacks,” said Francis deSouza, COO, and president, Security Products.

“Security must become an autonomous force, responding faster than the threat itself,” he said.

To help, we introduced three new agents in Google Security Operations to help you defend at the speed of AI.

Threat Hunting agent: Helps teams proactively hunt for novel attack patterns and stealthy adversary behaviors that bypass traditional defenses. Now in preview.
Detection Engineering agent: Identifies coverage gaps and creates new detections for threat scenarios. Now in preview.
Third-Party Context agent: Enriches workflows with contextual data from third-party content. Coming soon to preview.

You can also build your own security agents with remote Google Cloud MCP server support for Google Security Operations, now generally available, and access it from the Google Security Operations chat interface, now in preview.

Then there’s Wiz, now a part of Google Cloud, whose AI-Application Protection Platform (AI-APP), Wiz Security Agents, and Wiz Workflow help you identify and respond to risks and threats at machine speed. New in the Wiz family today are:

Secure vibe-coded applications: A new integration runs Wiz security scanning directly inside the Lovable platform so vulnerabilities, secrets, and misconfigurations caught by Wiz surface in Lovable's built-in security view. Generally available in May.
Secure AI-generated code: Wiz can now remove risks from AI-generated code with inline AI security hooks integrated directly into IDEs and agent workflows, injecting security guardrails before code is committed.
Agent-based remediation: Wiz Skills can equip coding agents and AI-native IDEs with full code-to-cloud context and validated attack surface findings from the Wiz Security Graph.
AI-Bill of Materials (AI-BOM): Work towards eliminating shadow IT by automatically inventorying all AI frameworks, models, and IDE extensions across your environment.
Google Cloud Fraud Defense: The evolution of reCAPTCHA, this platform is designed to discern the legitimacy and authorization of bots, humans, and agents. Now generally available.

Read more about these security innovations here.

6. Workspace Intelligence

For a look at AI for end-users, we heard from Yulie Kwon Kim, VP, Product, Google Workspace, who shared new ways that AI is manifesting in our collaboration and productivity suite. Workspace Intelligence is a unifying semantic layer that breaks down information and context silos for you and your agents. It understands your work, your priorities and the people you work with to help you get more done.

“Think of it as a unified intelligence layer that lives inside every Workspace app. It connects the dots and lets AI do the heavy lifting," Yulie said.

Here’s what’s new:

Ask Gemini in Google Chat allows you to instantly synthesize information, surface insights, and query projects from across Workspace directly from your Google Chat window. It provides proactive daily briefings to help you prioritize, and also lets you take immediate action — such as scheduling a meeting on your calendar or creating a Google Doc to develop a pre-meeting brief — turning your conversations into momentum without having to switch tabs.

Reimagined content creation in Docs, Sheets, and Slides uses Workspace Intelligence to synthesize information from across Workspace and the web and creates professionally formatted drafts that match your voice, style, and brand.
AI Inbox and AI Overviews in Gmail creates a personal, proactive inbox assistant with Gemini.
Google Drive Projects instantly organizes your team's files and emails to manage workflows, generate content, and deliver specific answers based on rich project context. In addition to newly added AI Overviews and Ask Gemini, Projects is another way we’re transforming Drive from a storage tool into an active collaborator to provide insights about your data.
Workspace agent in Gemini Enterprise executes complex, multi-step tasks across Google Workspace apps without having to leave Gemini Enterprise.

For more, check out the full Workspace Intelligence blog.

7. Agentic Commerce

For enterprises, AI agents are reshaping how consumers engage with companies and their products.

“In the agentic era, an agent isn’t just a tool; it’s a strategic extension of your business, built to expand your reach, deepen engagement, and personalize service at scale,” said Carrie Tharp, Google Cloud Vice President of Go To Market Strategic Industries.

Gemini Enterprise for Customer Experience offers a suite of tools to enhance the entire customer journey, from the first moment of discovery through on-going interactions that remember the customer like the best shopkeeper would.

Shopping agent and Food Ordering agent bring new conversational sales and ordering capabilities direct to businesses and third-party chat and digital interfaces.
Omnichannel Gateway helps agents maintain context across web, mobile, and voice, so a company’s agents can offer more personalized service.
Agent Assist helps during complex customer service situations, coaching employees to deliver fast and more accurate answers to customer questions by having organizational data readily available through gen AI grounding.

With Omnichannel Gateway in particular, about bridging the physical, digital, and agentic shopping experience, so consumers always have a familiar, brand-aware experience.

“If a customer moves from text chat to a phone call, the agent seamlessly remembers exactly where they left off,” said Carrie. Now that’s progress!

For more customer stories, check out all 1,302 of the latest gen AI use cases from businesses around the globe.

Innovate all the things

From new products, to new solutions, to new ways of working, there are so many other ways that we’re helping organizations take their AI from pilot to production.

Over the following week, we’ll share even more news, helpful how-to guides, and go deeper on today’s announcements. Stay tuned!

Converging operational and analytical data for AI transformation

Wed, 22 Apr 2026 12:00:00 +0000

To act at the speed of business, AI agents must operate in fast and trusted reasoning loops. They need to “think” by reasoning across both your historical context and your live operational reality. Only by understanding this complete, real-time picture can they “do” — taking immediate action.

For decades, data architectures have been built with a structural wall that breaks this loop, separating the platforms that generate insights from the platforms that manage actions. This latency means insights are gleaned after the critical window for an agent to take action has closed. Achieving true AI transformation requires organizations to move from a passive system of record to a proactive System of Action, built on a closed-loop architecture that converges operational and analytical data.

At Google Cloud Next, we announced new unifying capabilities that drive our Agentic Data Cloud, eliminating silos and enabling 98% of our largest data cloud customers to run operational and analytical workloads in a unified data platform. By operating AlloyDB, BigQuery and Spanner together, we are delivering an AI-native architecture that unlocks the full potential of your data for real-time, agentic applications.

Flexible, real-time data agents

To act effectively, agents require both operational and historical signals for sound decision-making. Our Agentic Data Cloud bridges the gap between the operational "now" and analytical history by handling the complex plumbing for you. We provide diverse integration models across data federation, reverse ETL, and real-time ingestion to the lakehouse, empowering your agents to make high-stakes decisions with both live context and historical depth.

For example, sometimes an agent driving a live operational application needs to pull historical context on demand. Through Lakehouse federation for AlloyDB (Preview), agents can access Lakehouse data directly from AlloyDB itself. This allows frontline systems to instantly query extensive historical data without relying on brittle data movement pipelines.

In other scenarios, the challenge is reversed: deeply complex historical insights have already been calculated in the data warehouse, but an agent needs to deliver them to millions of users at conversational speeds. Reverse ETL for BigQuery (Preview) provides a one-click solution to push these heavy analytical insights back into AlloyDB, Bigtable, or Spanner, enabling agents to serve them with sub-millisecond latency.

One-click reverse ETL

Teams running real-time analytics on live operational data typically have to move that data into analytical systems — an error-prone process that introduces lag. With Spanner Columnar Engine (GA) users can perform analytical queries that run up to 200 times faster with zero impact on production transactional workloads.

Finally, the reasoning loop is not complete until an agent’s real-time action is captured for downstream analysis. To close this loop, Datastream for Lakehouse Apache Iceberg tables provides real-time Change Data Capture (CDC) from AlloyDB, Cloud SQL, Spanner, and Oracle directly into the open Lakehouse. This process streams every operational change as an append-only event into Lakehouse tables, making that data immediately available in BigQuery for ML model training, feature engineering, and real-time analytics.

"AlloyDB, along with other Google Cloud products like BigQuery, provides the agility and performance needed to continually enhance our platform's capabilities and help us anticipate emerging trends rather than merely reacting.” - Javi Fernández, CTO, Loyal Guru

Grounding agents in a unified governance foundation

Inconsistent definitions and unclear data ownership across operational and analytical systems can cause agents to hallucinate. To address this, we are extending Knowledge Catalog (Preview), formerly Dataplex, with new integrations for AlloyDB, BigQuery, Bigtable, Cloud SQL, and Spanner to provide a unified map of your data landscape. Integrations with Oracle AI Database@Google Cloud and Firestore are coming soon. The Knowledge Catalog works by aggregating native context across your Google and partner data platforms, semantic models, and third-party catalogs, unifying them into a single, governed source of truth needed to build and scale reliable agents.

"Seven-Eleven Japan created “Seven Central,” a scalable data platform that uses Spanner and BigQuery to provide real-time insights and support the company’s digital innovation strategies. We collect data from all 21,000+ stores, and in anticipation of a future expansion in business operations, we have designed a system that can scale up and run without issue, even if we were to have 30,000 stores, with 1,000 customers per store per day."
-Izuru Nishimura, Executive Officer and Head of ICT Department, Seven-Eleven Japan

Unified engines for deep reasoning

To move beyond simple Q&A chatbots to autonomous agents, AI must reason across every dimension of your data estate. Historically, combining keyword search, semantic understanding, and relationship mapping meant moving data out of operational databases and into specialized, siloed search engines — introducing latency and complexity.

Google’s Agentic Data Cloud eliminates these silos. By embedding native vector and full-text search directly into operational databases like AlloyDB, Bigtable, Cloud SQL, Firestore, and Spanner, agents can execute highly accurate hybrid searches combining keyword relevance and semantic intent.

We’re also bringing together graph and vector support across BigQuery and Spanner. With graph federation, an agent can match live user intent in Spanner and immediately trace that intent through historical graph relationships in BigQuery Graph — accelerating autonomous decision-making without moving the data. This multi-model approach powers advanced GraphRAG patterns, equipping agents with the rich, interconnected context required to accelerate autonomous decision-making.

“To deliver AI that actually works across HR, payroll, and workforce operations, you need a consistent, real-time data layer. With the power of Google’s Agentic Data Cloud, People Fabric is the backbone of UKG’s Workforce Operating Platform — turning fragmented systems into a single source of truth that powers intelligent, agent-driven experiences.”
-Radhi Chagarlamudi, Group Vice President, Product Engineering, UKG

Built for performance at agent scale

Our Agentic Data Cloud delivers the closed-loop architecture required for the AI era without compromising operational performance. Built on open standards like Iceberg and PostgreSQL, and governed by universal semantics, Google Cloud provides the speed, throughput, and trusted context needed to build the next generation of conversational and autonomous applications.

Build: Explore the AlloyDB AI documentation to start grounding your agents.
Connect: Visit the BigQuery Console to set up your first federated query to Spanner.
Govern: Opt-in to the Knowledge Catalog for unified visibility.

The future of data lakehouse: Open and interoperable for the agentic era

Wed, 22 Apr 2026 12:00:00 +0000

Traditional lakehouses were engineered for the era of reporting, not the high-velocity, multimodal demands of AI agents. To bridge this gap, architecture must evolve into an AI-native foundation — one that replaces batch processing with continuous feedback loops and live data streams. This shift gives agents the reliable context they need to transform raw data into action and unlock all enterprise data (structured and unstructured) across cloud boundaries.

Today, we announced our next-generation cross-cloud Lakehouse that delivers four core breakthroughs:

Fully managed Iceberg storage with enterprise-grade features, giving you the benefits of open-source flexibility plus performance, scale, governance, and multimodal processing.
New cross-cloud interoperability, bringing Google’s high-performance, scalable foundation and AI capabilities to your data, supporting an expansive data ecosystem.
A high-performance Apache Spark experience, accelerating your data science workloads with exceptional performance and your choice of developer environments.
AI-powered, always-on context, enabling AI agents to reason in real time across operational and analytical data.

This agentic-first lakehouse approach can deliver an estimated 117% ROI with payback in under six months. Spotify is already unlocking innovation with Google Cloud’s Lakehouse.

“Spotify is leveraging Google Cloud’s Apache Iceberg products as part of our efforts to build a truly modern data lakehouse that removes the silos between our data lakes and warehouses. This architecture provides us with an interoperable and abstracted storage interface, allowing our teams to process the same data across BigQuery, Dataflow, and other open-source engines without duplication. It will simplify our governance and unlock the ability to innovate at a scale that was previously impossible.”
— Ed Byne, Product Manager, Spotify

Accenture, a key partner in this journey, sees this as a fundamental shift in how enterprises operate:

“To reinvent enterprise operations, organizations must collapse the data boundaries that fragment their intelligence. By utilizing the Google Cloud Lakehouse and 'zero-copy' innovation, we can help customers activate agentic AI with surgical precision. Whether leveraging high-performance Apache Spark for complex data science or delivering scale for industries like retail and life sciences, this AI-native foundation transforms trapped data into real-time action."
— Scott Alfieri, Global Lead, Accenture Google Business Group

Openness without compromise

With Google Cloud’s unique, vertically integrated infrastructure, you get the open-source flexibility of Apache Iceberg backed by a fully integrated, managed data-to-AI experience. You can manage all your multimodal data with unified governance, and get your data estate ready for agents with always-on context. By connecting your Iceberg tables directly to engines like BigQuery and Managed Service for Apache Spark, you can accelerate your AI workloads in real time.

Today, we announced four new innovations to make your Iceberg experience on Google Cloud even stronger:

Fully managed Iceberg storage with read/write interoperability: Experience unified Apache Iceberg tables, managed via the Lakehouse runtime catalog (formerly BigLake metastore). This provides read and write interoperability between BigQuery and Managed Service for Apache Spark including Iceberg-compatible OSS engines like Spark, Trino, and Flink, and third-party engines like Databricks and Snowflake (Preview).
The power of BigQuery with Iceberg: Access advanced runtimes, automatic table management, partitioning, multi-table transactions, and history-based optimization for REST catalog tables (Preview) and Iceberg tables managed by BigQuery Catalog (GA).
A unified multimodal foundation: Use BigQuery ObjectRefs (GA) to merge unstructured data in Cloud Storage with structured data in Iceberg. This simplifies multimodal analysis and manages conversational insights through BigQuery AI.
Unified management and governance: Enhance enterprise trust with open lakehouse governance (Preview) via Knowledge Catalog (formerly Dataplex). Securely ground your agents with business context using end-to-end data lineage, search, quality profiling, and table-level access controls for your Iceberg estate.

Cross-cloud power without the friction

Cross-cloud is today’s enterprise reality, and your agents need a scalable solution to work across data, no matter where it is. Production cross-cloud data access often fails to scale due to high egress overheads and performance bottlenecks. So we are introducing a new high-performance, scalable cross-cloud experience by bringing Google’s AI capabilities to your AWS and Azure data that delivers similar price-performance characteristics to cloud-native solutions:

A new AI-native cross-cloud lakehouse: Lakehouse cross-cloud interconnect and cross-cloud caching (Preview) provides BigQuery and Managed Service for Apache Spark (formerly Dataproc) with high-performance access to AWS Iceberg data at scale. This new capability, powered by high-throughput, low-latency, cross-cloud connectivity and advanced cross-cloud query processing innovations, delivers price-performance characteristics similar to AWS-native data platform solutions. You can run Gemini-powered use cases, such as building agents in Gemini Enterprise and BigQuery AI functions, over your Amazon S3 Iceberg data.
An interoperable ecosystem powered by open standards: To help you easily discover and analyze all your enterprise data across any engine or cloud, we are launching Lakehouse catalog federation (Preview) for AWS Glue, Databricks, SAP, and Snowflake, with Confluent Tableflow coming later this year. This foundation enables simple access to data across clouds through BigQuery and Managed Spark, supported by an expanding partner ecosystem that includes bi-directional access for Databricks, Oracle Autonomous Database, and Snowflake pipeline support for dbt, serving with Clickhouse, and catalog integrations with Atlan and Datahub (preview). And, advanced Lakehouse Governance (Preview), guarantees that security protocols and access permissions are immediately enforced throughout this unified environment.

High-performance Spark, built for enterprise scale

Managed Service for Apache Spark offers a unified, high-performance experience that accelerates everything from data engineering to agentic AI development. It empowers data teams to extract maximum value from their enterprise data without friction by delivering key advantages:

Frictionless, agentic data science: Customers can gain a highly flexible data science environment, integrating Colab Enterprise, Gemini Enterprise, and local IDEs with BigQuery and managed Spark. This allows developers to run Python, Spark, and SQL on a single, unified copy of data, eliminating movement and optimizing engine choice while the Lakehouse runtime catalog Iceberg REST catalog endpoint (Preview) automates table management.
Better Spark processing: Lightning Engine for Apache Spark delivers up to 2x the price-performance over the leading high-speed Spark alternative. This engine uses vectorized execution, intelligent caching, and optimized I/O to provide industry-leading performance on Iceberg, Parquet, and Delta formats without requiring any code changes.

Built for the scale and speed required by agentic AI

Google Cloud’s Lakehouse is a high-performance, real-time foundation businesses can use to scale in the agentic era. We use AI to discover hidden relationships within your enterprise data to provide 24/7 curated context for your agents, making it easier to activate them instantly using databases with new integrations. This includes:

Always-on context for agents: Knowledge Catalog (formerly Dataplex) builds a unified foundation by aggregating business context from your entire data landscape, including Iceberg. It delivers continuous enrichment by learning how your enterprise actually uses data, using Smart Storage to automatically map complex relationships within unstructured files. To guarantee trust and relevance, it uses access-control-aware, high-precision search powered by Google Search innovations. This instantly identifies trusted context and feeds it to AI agents, ensuring they deliver reliable, grounded results.
Ready-to-use BigQuery and Looker agents: BigQuery provides built-in Conversational Analytics, a Data Engineering Agent, and a Data Science Agent (Preview) that work with your cross-cloud, multimodal data. Looker Conversational Analytics provides insights in natural language to your business users. You can build your own agents with your lakehouse as the foundation with Google-native tools like Agent Developer Kit (ADK) and Model Context Protocol (MCP).
Real-time analysis and agentic activation of operational data: Integrate operational data into your lakehouse. Spanner, AlloyDB, and Cloud SQL support real-time change replication into BigQuery (GA) and Iceberg (Preview). Analytical data in Iceberg can also be served with low latency using AlloyDB and Spanner (Preview).

Get started today

Built for the AI era, our cross-cloud Lakehouse delivers uncompromising open Iceberg storage, enables multi-cloud interoperability, and equips your AI agents with always-on context for closed-loop activation. Learn more and start building today.

Google Cloud and SAP unveil blueprint for the Agentic Enterprise

Wed, 22 Apr 2026 12:00:00 +0000

Google Cloud and SAP are deepening their partnership, working together to embed Gemini AI directly into the core business processes of the world's largest enterprises.

A new foundation for AI: Today at Google Cloud Next '26, we are joining SAP to showcase the latest breakthroughs for the Agentic Enterprise from our ongoing partnership.

These include the Unified Data Foundation, which integrates SAP and non-SAP data into a bi-directional source of truth, transforming routine cloud modernization into a strategic engine for data-driven value. Our new zero-copy data sharing capabilities further simplify architectures by removing the friction of data movement, ensuring the high-fidelity data access and real-time reliability essential for building mission-critical AI workloads.

We’re also supporting new capabilities in the SAP Engagement Cloud to deliver the latest in AI creation, ideation, and learning tools. These platforms are available not only to SAP employees but also as an offering for SAP’s own customers. This suite of AI tools will help drive agentic understanding and adoption across the enterprise.

Why it matters: This shift closes the gap between data storage and AI innovation. It empowers organizations to move from simply managing infrastructure and start deploying intelligent agents that can autonomously execute complex, multi-step tasks.

The Unified Data Foundation in particular offers not only valuable insights for existing workflows but also helps power the outputs of AI tools and agentic workflows.

The big picture: The deepening partnership is already transforming how SAP delivers value to its own customers.

Driven by an internal mandate to maintain market relevance and build a competitive advantage, SAP's Engagement Cloud division partnered with Google Cloud to build next-generation agentic solutions. These include agents for dynamic content development, generation of marketing briefs and visual concepts, and collaborative multi-agent execution.

What they're saying: “Agentic AI only creates value when it’s grounded in trusted data and directly connected to execution, which is what makes our partnership with Google Cloud so exciting for businesses. By embedding generative and agentic intelligence directly in Engagement Cloud, Google and SAP are giving marketing teams a practical way to turn real-time intelligence into meaningful customer interactions.” – Joanna Milliken, Head of Engagement Cloud, SAP

What's new:

The zero-copy revolution: SAP BDC Connect for BigQuery delivers bidirectional, zero-copy data sharing. You can unify your entire data footprint without moving or duplicating massive datasets.
Cortex Framework: New solution accelerators embed rich metadata directly into BigQuery. This grounds Gemini agents in an accurate enterprise context, heavily mitigating AI hallucination risks.
Elevated 99.95% SLA: An industry-leading uptime guarantee for RISE with SAP on Google Cloud, driven by AI-powered predictive outage prevention.
SAP Sovereign on Google Cloud: Customers with strict data residency mandates can now run SAP's S/4HANA Private Cloud Edition directly on Google's sovereign infrastructure.

By the numbers: Customers can achieve up to a 54% lower total cost of ownership (TCO) for data analytics. Hosting SAP workloads on Google's energy-efficient public cloud also instantly accelerates corporate ESG targets.

What's next: SAP BDC Connect for BigQuery will soon be available in select data centers, with broader availability in the second half of 2026.

Go deeper:

Oracle AI Database@Google Cloud: The foundation for the Agentic Enterprise

Wed, 22 Apr 2026 12:00:00 +0000

For decades, many of the world’s most critical enterprise datasets have relied on the performance of Oracle databases. Today, we are bringing that reliability even closer to the cutting edge. By enabling customers to run Oracle AI Database services natively within Google Cloud, we’ve bridged the gap between foundational data and the modern AI stack.

With the latest wave of upcoming launches for Oracle AI Database@Google Cloud, we aren't just making it easier to migrate; we are building a direct pipeline from your Oracle systems of record to the insight layer of Google Cloud. By bringing mission-critical data easily and securely to Gemini models and BigQuery, customers can transform static records into autonomous, agentic workflows.

New capabilities announced at Next ‘26

Here is a breakdown of the key new features designed to strengthen your Oracle-to-agentic- AI strategy:

New regions launched: We have significantly expanded the availability of Oracle AI Database@Google Cloud, across 15 regions (and 20 sites) globally. The recent rollout included key global hubs such as Milan, Iowa, São Paulo, Tokyo, Sydney, and Mumbai, among others. With additional regions like Mexico and Turin coming soon, this expansion ensures higher availability and lower latency for your mission-critical workloads across the globe for our Google Cloud customers.
Enhanced AI capabilities: This is the foundation for agentic AI. We are introducing the preview of Managed MCP Server for Oracle workloads, which allows agents like Gemini to interact directly and seamlessly with your Oracle infrastructure. Building on this, the new Oracle AI Database Agent, available in the Google Cloud AI Agent Marketplace, lets you talk to your Oracle data directly from Gemini Enterprise — no custom chatbot or NL-to-SQL solution required.
Database Center integration (Generally Available): To move at the speed of AI, your infrastructure must be healthy and visible. Database Center now supports Oracle AI Database@Google Cloud, providing a "single pane of glass" for your entire data estate. Whether you are running Exadata or Autonomous Database, you can now monitor your inventory and streamline operations through a unified experience within the Google Cloud console.
Knowledge Catalog integration (Preview): Data discovery is the first step toward intelligence. By extending the Knowledge Catalog to Oracle AI Database@Google Cloud, we are breaking down the walls between your Oracle systems and the Google Data Cloud. This allows for a unified governance and metadata layer, making it easier for customers to find, trust, and use Oracle data and provide context to AI agents.
OCI GoldenGate Service integration (Preview): Real-time data is the lifeblood of AI. This integration enables low-impact, continuous data movement, allowing you to streamline migrations from on-premises environments to Oracle AI Database@Google Cloud. In addition, it provides a live link to BigQuery, enabling operational data analytics that reflect the "here and now" of your business.
VPC Service Controls: Oracle AI Database@Google Cloud administrators can use VPC Service Controls to restrict access to the admin API and create databases within a service perimeter. VPC Service Controls protect businesses from unauthorized access outside the security perimeter, even if credentials have been compromised.

The agentic future

The goal of these integrations is simple: To make your data active. When your Oracle data resides natively in Google Cloud, Gemini doesn't just “talk about” your data — it can work with it. Whether it's an AI agent forecasting supply chain shifts in BigQuery based on live Oracle ERP feeds, or a customer service bot with real-time access to legacy account history, the data vault is more open, accessible, and valuable than ever (while remaining just as secure).

Hear directly from our customer, Banco Actinver, regarding the transformative impact of relocating their Oracle data to Google Cloud.

Get started

You can access Oracle AI Database@Google Cloud through the Google Cloud Marketplace using your existing Google Cloud account and billing.

For more information, visit:

Welcome to the agentic BI era with Looker

Wed, 22 Apr 2026 12:00:00 +0000

By combining the analytical depth of Looker with Google’s Agentic Data Cloud, the potential to transform how we model, interact with, and act on our data appears limitless. This week at Google Cloud Next, we are thrilled to share how we are reimagining the business intelligence (BI) stack with Looker for the AI era, starting with a deeper integration with BigQuery.

Our product strategy is focused on delivering trusted data and prescriptive insights for everyone to take actions through Agentic BI.

We’ve rebuilt Looker with Gemini to power a new era of AI agents. With Looker providing the trusted data foundation, your team can innovate rapidly without losing accuracy.

Conversational BI and agents: From conversations to actions

At Next ‘26, we are announcing Looker BI Agents that don’t just provide static answers, but trigger downstream business actions grounded in the Looker semantic layer and your existing enterprise governance framework.

Today, we introduced a number of new agents in Looker:

Dashboard Agents - getting answers to your data directly within a dashboard.

Conversational Agents upgrades (Preview): We are significantly upgrading our core Conversational Analytics agent, which is already GA, with superior reasoning and semantic grounding, helping to eliminate ambiguity. New visibility tools provide admins with end-to-end observability, so that they can monitor performance trends and refine model accuracy at scale. Teams can now also publish Conversational Analytics in Looker directly into Gemini Enterprise to incorporate trusted data into broader workflows.
Dashboard Agents (Preview): Dashboard Agents bring conversational capabilities directly into your BI workflows, providing instant summaries and the ability to ask questions directly within a dashboard.
Embedded Conversational Experiences (GA): You can now embed Conversational Analytics agents directly into your custom applications and internal workflows, allowing users to query data via the UI or API without leaving their specialized tools.
Agentic Workflows in Looker (Preview): Agents can now monitor critical metrics for irregularities and identify hidden correlations and "what’s next" recommendations, so you can address shifts in the business — before they impact the bottom line.

“At YouTube, we're focused on helping creators succeed and bring their creativity to the world. We've been testing Conversational Analytics in Looker to give our partner managers instant, actionable data that lets them quickly guide creators and optimize creator support." - Thomas Seyller, Senior Director, Technology & Insights, YouTube Business

Reimagining self-service: AI power, UI control

AI powered Google-easy exploration for ease and simplicity.

We are making Looker more intuitive and easy for both business users and analysts.

Additionally, we are introducing new AI assistants in Looker Explores to erase the hard parts of BI and transform complex workflows into natural language interactions that feel as familiar as a Google search. New today are:

The new and modernized Looker interface (Preview): A modernized, Google-easy drag-and-drop interface for AI-driven self-service Explores, featuring tabbed dashboards (GA) and paginated reporting (GA).
Visualization Assistant (GA): Use natural language to create and refine charts. Simply ask to change drafted visuals to a stacked bar chart or color-code by region, and Gemini handles the rest.
Expression Assistant (Preview): Generate custom dimensions, measures, and filters just by describing your desired logic — no more having to remember syntax!
Insight Assistant (Preview): Beyond just showing data, Insight Assistant automatically generates summaries and highlights key trends within your reports, helping you identify the so what in seconds.
Self-service Explores (GA): Blend personal data from CSV and Excel files with your enterprise data, while a new interface makes it easier to build models visually. Behind the scenes, Looker handles the LookML code. Together, they provide the agility of a spreadsheet with the rigorous version control of an enterprise platform, so that even ad-hoc data analysis remains governed.

“Our users (or customers) love the flexibility of Excel but fear the lack of governance. Looker’s Self-service Explores give them the best of both worlds. They can upload ad-hoc CSVs for a quick 'what-if' analysis, but because that data is analyzed alongside our LookML dimensions, we know the underlying business logic is still accurate.” - John Pettit, Chief Technology Officer, Promevo

A trusted and open platform

Having a rock-solid, governed semantic layer is a strong way to prevent AI hallucinations and ensure a single source of truth, and we have some exciting announcements in this area:

Open BI and MCP (Preview): Looker supports open-source Model Context Protocol (MCP) via MCP Toolbox, and now we’re offering an all new managed MCP server that’s native to Looker, for ease of customer management. Through this open BI approach you have the flexibility to choose how you deliver AI transformation to your users.
Looker Extension for VS Code (Preview): We are accelerating the developer lifecycle with a new intelligent setup wizard and a specialized LookML AI Agent that translates business intent directly into production-ready code. This new plugin for VS Code and Agentic IDEs lets you vibe-code with LookML, delivering an AI-powered authoring experience for building LookML models simply by describing your desired outcome.

Updated LookML with Gemini support in the Gemini-powered IDEs

Continuous Integration (CI/CD) (GA): Now generally available, this fully integrated advanced CI capability in Looker automates SQL validation and content testing within your development workflow. By identifying potentially breaking changes before they reach production, Looker helps ensure model updates are reliable and accurate.
Knowledge Catalog integration (Preview): With Knowledge Catalog, Looker transforms metadata into a semantic graph, providing the essential context AI agents need to complete tasks autonomously.

The future of data isn't just about seeing insights — it's about acting on them. By combining the power of Gemini with Looker’s trustworthy semantic layer, we are making data-driven actions a reality for every user, in every workflow, every day.

Welcome to the era of Agentic BI.

Get started in your journey to transform your business through the agentic era with Looker at cloud.google.com/looker or start your developer experience at https://docs.cloud.google.com/looker/docs/api-sdk.

What’s new in BigQuery: Powering the Agentic Era

Wed, 22 Apr 2026 12:00:00 +0000

Succeeding in the agentic era requires a transformation in your data strategy: moving from human-scale to agent-first workloads, evolving from reactive intelligence to proactive action, and shifting from raw data to semantic knowledge that agents can use to reason accurately.

For over a decade, BigQuery’s continuous innovations have helped tens of thousands of organizations build a scalable data and AI foundation and navigate several industry and technological transformations. BigQuery has evolved into an autonomous data-to-AI platform, growing over 30x in data processed with Gemini, 25x in AI functions processing unstructured data, and 20x in agent-building tools with Model Context Protocol (MCP).

Customers like Definity are building data platforms to enhance their customers’ experience, improve back-office operations, and boost data team productivity.

“We stood up our data platform in Google Cloud and ingested all critical insurance data in 10 months, which is about half of the time that people see in the industry. The technology that BigQuery provides, processing large amounts of data very quickly, is giving our practitioners and engineers tools that are advanced and a platform that has AI and ML built in. We have doubled the number of users [in a very short period of time].” — Tatjana Lalkovic, Chief Technology Officer, Definity

Today, we are announcing new BigQuery capabilities in lakehouse, built-in AI processing and reasoning, and agentic experiences, all anchored by our commitment to industry-leading price-performance and enterprise readiness.

Open, cross-cloud lakehouse

Enterprise data is often scattered across applications, multiple cloud environments and on-premises. While early lakehouse solutions reduced data duplication, the agentic era demands a foundation that is natively multimodal, cross-cloud, and AI-ready. Our approach blends Apache Iceberg’s interoperability and Google’s differentiated infrastructure with new capabilities, including:

Managed Iceberg tables in Lakehouse (GA, formerly BigLake) enables the openness of Iceberg with advanced BigQuery capabilities, including automatic table management, Iceberg partitioning, multi-table transactions, change data capture, enhanced vectorization, and history-based optimizations.
Iceberg REST catalog enables read/write interoperability (preview) on Iceberg tables between BigQuery, Spark, and other OSS and third-party engines, so you don't have to make complex engine trade-offs.
Cross-cloud Lakehouse (preview) brings BigQuery AI and analytics to other clouds, starting with AWS and Azure. Using open standards like the Iceberg REST Catalog, high-bandwidth networking via Cross-Cloud Interconnect, and transparent caching, BigQuery achieves performance and total cost of ownership comparable to native warehouses, enabling true cross-cloud for enterprises.
Catalog federation (preview) enables easy discovery, analysis, and zero-copy sharing of data across AWS Glue, Databricks, SAP, Salesforce, Snowflake, and Confluent Tableflow (coming later this year).
Real-time data replication that closes the loop between raw data and operational action by allowing you to replicate data from Spanner, AlloyDB, and Cloud SQL instantly into BigQuery tables (GA) and Iceberg (preview).

Analyze data across BigQuery and Iceberg table on AWS using SQL or natural language

Refer to this blog to learn more about our latest lakehouse innovations.

Graph-based reasoning for enterprise agents

For AI to solve truly complex, multi-hop operational problems, business logic must extend all the way down to the data platform layer. Defining logic at this layer ensures that definitions remain consistent and governed from ingestion to consumption. BigQuery Graph (preview) provides the foundation to activate this context, allowing data practitioners to map entities, relationships, and business logic directly within the data platform. This anchors AI agents in a governed reality, enabling them to solve sophisticated challenges at scale with consistent accuracy.

Today, we are announcing new graph capabilities to enhance AI reasoning:

Native support for measures in BigQuery Graph (preview) enables you to unify analytical metrics and relationships into a single, governed entity. It transforms data into a "business map" for multi-hop structural reasoning, allowing agents to move beyond simple searches to trace the ripple effects of business events.
Graph support in BigQuery Conversational Analytics (preview) allows conversational analytics agents to navigate a deterministic business map instead of raw tables to provide answers with higher accuracy. Graphs enable dual reasoning: agents can instantly calculate precise KPIs using measures while simultaneously traversing complex relationships to uncover the "why" behind the numbers.
BigQuery Graph and Looker integration (preview) allows you to reuse measures defined in BigQuery across your data stack by exposing graphs as Looker views. You can also define BigQuery Graphs using Looker with source control and validation. This interoperability ensures that metrics, such as Churn Rate, remain identical across both dashboards and AI agents.
Visual modeling experience in BigQuery Studio (preview) provides an intuitive interface to easily build and manage the entities, relationships, and business logic that power your agentic context.

Analyze data in natural language using Conversational Analytics Agents with BigQuery Graph

Native AI processing to unlock structured and unstructured data

Your data is no longer confined to rows and columns; agents demand a platform that can work across structured and unstructured data at scale without requiring data copies or movement. BigQuery AI makes it easy to perform predictive machine learning tasks (like forecasting, anomaly detection, and recommendations) and generative AI tasks (like entity extraction from images and text, content generation, and data enrichment), with built-in access to over 170 foundational models. New capabilities in BigQuery AI include:

AI.PARSE_DOCUMENT (preview) simplifies complex document processing workflows with a single SQL function that automates Optical Character Recognition (OCR), layout parsing, and chunking.
TabularFM model (preview) brings high-quality regression and classification to BigQuery without the need for extensive feature selection, tuning, training or model management.
ObjectRef (GA) allows you to process unstructured data alongside structured data using SQL and Python. This establishes the foundation for building rich, multimodal context directly on your Knowledge Catalog.
Optimized mode (preview) for SQL-first, AI co-processing-managed functions like AI.CLASSIFY and AI.IF trains task-specific models on the fly, delivering a 230x reduction in tokens consumed compared to row-by-row gen AI processing.
BigQuery-native Gemma embeddings (preview) allow you to generate high-quality embeddings at scale on standard CPUs.
Autonomous embedding generation (GA) fully manages the pipeline for unstructured data, automatically, keeping vector indexes in sync as new data gets ingested.
BigQuery hybrid search (preview) unifies retrieval by integrating semantic and full-text search into a single function, delivering superior precision for RAG and complex exploration.
Python UDF (GA) allows you to enrich, transform or clean data with fully managed Python scalar functions. You can bring your own code or libraries and the functions will autoscale to millions of rows with serverless, scale-out execution. This will be rolled out in phases over the next few weeks.
Connected Sheets brings the scale of BigQuery to the familiar Google Sheets interface, now supporting forecasting with TimesFM model (GA) and anomaly detection (preview).
Geospatial analytics datasets (preview) can now be accessed directly from BigQuery, allowing you to combine geospatial data — such as infrastructure assets and road management insights — with your enterprise data for deeper analysis, without complex manual data wrangling.

Access, process and activate unstructured data in BigQuery

Agentic experiences

AI enables highly skilled data teams to focus on high-impact strategic initiatives by offloading tedious and time-consuming tasks. We are pioneering this space by integrating Google’s foundational research, capable models, and specialized tools directly into BigQuery, providing automation and assistance at every stage of the data lifecycle. New agentic experiences in BigQuery include:

Conversational Analytics in BigQuery (GA) allows teams to query complex datasets using natural language. It provides a secure and transparent path to insights while supporting advanced features like predictive analytics and reasoning across structured and unstructured data. In addition to BigQuery Studio, BigQuery Conversational Analytics agents can be published to and consumed from Data Studio (Preview), Gemini Enterprise (Preview), or via an API for custom applications (GA).

Gain insights from your data using natural language with Conversational Analytics in BigQuery

Proactive agentic workflows (preview) go beyond simple questions to detect metric shifts, perform root-cause analysis to explain why a change occurred, and deliver scheduled research briefings directly to your inbox.

Proactive agentic workflows in BigQuery

BigQuery Agent Analytics offers plugins for ADK (GA) and LangGraph (Preview) frameworks to record agent activity to BigQuery and analyze it for troubleshooting, optimization, and agent evaluation.
BigQuery Studio gets new productivity tools, including a contextually aware assistant (preview) for resource discovery and troubleshooting, SQL Cells (GA) for blending SQL and DataFrames, and Visualization Cells (GA) to create visuals directly within notebooks. The Files Explorer (GA) allows developers to organize, share, and manage code assets in folders. Additionally, we are introducing Git integration and workflows (preview) in BigQuery Notebooks, providing complete SCM coverage across GitHub, GitLab, Bitbucket, and Azure DevOps for data science workflows.
Data Science Agent (GA) in BigQuery notebooks allows you to simply state goals in plain English to automatically execute plans for loading, cleaning, and visualizing data using BigQuery ML, DataFrames, or Spark.
Data Engineering Agent (GA) - develop, manage, migrate, and troubleshoot data pipelines in BigQuery. It uses your custom context through instructions and integration with the Knowledge Catalog and is available in BigQuery Pipelines and Dataform.
Colab Data Apps (preview) bridge the gap between analysis and action by transforming notebook analyses into shareable, fully managed interactive Python applications that business teams can access from Data Studio.
BigQuery remote MCP server (GA) and BigQuery ADK toolset (GA) reduce the need for manual database connectors for agents.
Google Cloud Data Agent Kit (Preview) provides a portable suite of skills, Model Context Protocol (MCP) tools, environment-specific extensions, and native plugins. By meeting you where you build, like VS Code, Gemini CLI, Codex, and Claude Code, the Data Agent Kit turns your IDE, notebook, or terminal into a native data environment. This enables your environment to orchestrate a massive range of data workflows, automatically selecting the right frameworks (like BigQuery, dbt, Apache Spark, or Apache Airflow) and generating production-ready code.

Unparalleled performance and scale

Modern analytical workloads are increasingly unpredictable and distributed. We continue to invest in enhancing BigQuery's core engine to ensure that as data grows in your environments, your costs and operational overhead do not.

Fluid scaling (GA) enables you to execute highly variable workloads with a premier autoscaling model that does not require a cost-and-performance trade-off. Fluid scaling in BigQuery enables true per-second billing, offering up to 34% cost savings.
Advanced runtime, small query, and history-based optimizations (GA) accelerate native and Iceberg workloads without code or schema changes. BigQuery has improved query speed by 35% year over year while reducing query processing costs by 40% year over year.
New workload management features — including reservation groups (GA), flexible dynamic assignments (preview), and project-level slot and concurrency controls (preview) — provide granular cost attribution and price-performance control, all simplified by declarative, rules-based workload management (preview).
Enhanced observability (GA) and intersection routing (preview) provide flexible disaster recovery capabilities for mission-critical workloads. Agent-powered observability (preview) for turnkey troubleshooting further simplifies operations, while the agent-ready security center (preview) provides a unified, fine-grained access control experience.

BigQuery fluid scaling for unpredictable workloads

In the agentic era, BigQuery isn’t just where your data lives — it’s where your data thinks, reasons, and acts. The future is here, and it’s more open, autonomous, and capable than ever.

Get started on your data and AI journey by taking advantage of our BigQuery data migration offer. We can’t wait to hear how you’re innovating with data.

What’s new in the Agentic Data Cloud: Powering the System of Action

Wed, 22 Apr 2026 12:00:00 +0000

Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structural failures that can lead to fractured governance, a persistent trust gap, and broken reasoning loops, all while causing costs to spiral.

To solve this, we’re introducing the Agentic Data Cloud: an AI-native architecture that evolves the enterprise data platform from a static repository into a dynamic reasoning engine. It closes the gap between thinking and doing, allowing AI agents to act on your business data and context. While last-generation systems of intelligence were built only for human scale, the Agentic Data Cloud is a System of Action, built for agent scale.

Leading organizations are already using the Agentic Data Cloud to deliver tangible value:

Vodafone has launched hundreds of agents to deliver uninterrupted service to their customers, which is expected to save them millions of euros every year.
American Express is moving a core on-premises data warehouse and hundreds of production applications to BigQuery to power trusted agentic commerce at scale.
Virgin Voyages is using over 1,000 specialized AI agents, including one that slashes mass itinerary rebooking from six hours to just 11 minutes.

Today, we’re announcing three new innovation areas powering our Agentic Data Cloud:

A universal context engine that provides agents with trusted business context to drive higher accuracy.
Agentic-first practitioner experiences to evolve the role of data practitioners and developers as orchestrators of agents.
An AI-native, cross-cloud lakehouse that eliminates data silos by connecting your entire data estate.

Enabling agents with a universal context engine

AI is only as smart as its context. If an agent doesn't understand your definition of "margin" or the intricate relationships in your supply chain, it’s forced to guess. In the age of the agentic enterprise, data alone is not enough, and the old model of governance is insufficient. This is why we evolved the Dataplex Universal Catalog into the Knowledge Catalog, which maps and infers business meaning across your entire data estate, using a rigorous framework of aggregation, continuous enrichment, and search.

Here’s how it works:

Aggregation: To build true context, you must bring it together from everywhere. We are aggregating native context across your Google Cloud and partner data platforms. This includes third-party catalogs, applications, operating systems, and AI platforms like Palantir, Salesforce Data360, SAP, ServiceNow, and Workday (Preview). By using our Lakehouse, your third-party data assets are automatically mapped to the Knowledge Catalog. For Google Cloud sources, we are automating business logic with the new LookML Agent (Preview) and BigQuery measures (Preview), which embeds that business logic natively into the platform.
Continuous enrichment: The Knowledge Catalog delivers continuous enrichment by analyzing usage logs across your organization and profiling data behind the scenes. It learns how your enterprise actually uses data, not just what it is. This extends to unstructured data. The moment a file lands in Google Cloud Storage, our Smart Storage (Preview) instantly tags and enriches images and, soon, PDF objects. The Knowledge Catalog also identifies useful collections of unstructured data and uses Gemini to automatically generate missing schemas, mapping complex relationships so your AI is no longer flying blind.
Search and retrieval: Creating a massive context layer is great, but in the agentic era, search has evolved to be the new query path. The hardest problems at enterprise scale are speed, relevance, global reach, and security. To solve this, the Knowledge Catalog uses a sophisticated hybrid search stack built on Google Search innovation. To deliver relevance, it combines semantic and lexical matching with intelligent, machine-learning-based re-ranking. To deliver trust, we are enforcing your security permissions natively with access-control-aware search, so agents can only retrieve and act on the assets they are authorized to see. This high-precision infrastructure instantly identifies trusted context and feeds it to specialized agents.

The Knowledge Catalog now powers the Deep Research Agent (Preview). Part of the suite of Google-made agents available in Gemini Enterprise, this agent can perform multi-step reasoning across Google Cloud data platforms, such as BigQuery, as well as internal documents and web assets to answer complex questions with citations and precision that previously required weeks of manual effort.

Agentic-first practitioner experiences

As we move to this new architecture, the data practitioner role shifts from writing manual pipelines to orchestrating intent-driven engineering.

We’re accelerating this transition with the Google Cloud Data Agent Kit (Preview). Rather than introducing a new interface, we are launching a portable suite of skills, tools, environment-specific extensions, and built-in plugins, that drop into the environments developers love. By meeting practitioners where they already build — including VS Code, Gemini CLI, Codex, and Claude Code — we turn your IDE, notebook, or agentic terminal into a native data environment. This enables your environment to autonomously orchestrate a wide range of business outcomes, automatically selecting the right frameworks (e.g., dbt, Apache Spark, or Apache Airflow) and generating production-ready code based on Google’s gold standards.

This kit doesn't just connect tools — it injects high-performance capabilities directly into the developer's flow, scaling to petabytes without moving data. In fact, the Data Agent Kit features the same skills and tools that power our own out-of-the-box agents, including:

Data Engineering Agent (GA): Builds complex pipeline transformations from scratch and enforces governance rules to keep bad data out of production.
Data Science Agent (GA): Automates the model lifecycle — from wrangling to training — scaling across BigQuery Dataframes and Serverless Apache Spark.
Database Observability Agent (Preview): Acts as a 24/7 guardian for your infrastructure, diagnosing root causes and executing database remediations.

To help ensure the smooth execution of agents, Google Cloud has fully embraced Model Context Protocol (MCP), which provides a secure, universal interface that allows any agent to discover and use your data assets across our core engines, including: BigQuery, Spanner (Preview), AlloyDB, Cloud SQL (GA), and Looker MCP (Preview). MCP for Google Cloud uses our security stack, governing agent interactions based on your existing IAM policies, VPC Service Controls, and data residency requirements.

We’re also reimagining the business user experience with Conversational Analytics, now supported across BigQuery (GA), Cloud SQL, Spanner, AlloyDB (Preview), and Looker (GA). Organizations can simply publish these custom analytical agents in Gemini Enterprise, enabling employees to chat with live data in a familiar interface. By removing the technical barriers, we’ve eliminated the weeks spent waiting for manual reports, allowing businesses to move at the speed of thought.

A cross-cloud foundation built for agentic scale

For an agent to act, it must have a fundamentally open foundation. If an agent is blocked by cross-cloud latency or trapped in a proprietary walled garden, its autonomy is broken. That’s why we are introducing a truly borderless, cross-cloud Lakehouse that liberates your data wherever it resides by:

Connecting analytical estates: We’re integrating Cross-Cloud Interconnect (CCI) directly into our data plane. By combining CCI’s dedicated, high-speed private networking with Apache Iceberg REST Catalog, we’re enabling connectivity across clouds that is low latency and eliminates massive egress fees. As a result, agents can use data across AWS and Azure as if it were local to Google Cloud with seamless cross-cloud access.
Ending proprietary silos: We’re championing open federation to end the era of proprietary catalogs by launching bi-directional federation (Preview). Powered by the Iceberg REST Catalog, engines can now read directly from Databricks Unity Catalog on Amazon S3 (Preview), Snowflake Polaris (Preview), and the AWS Glue Data Catalog on Amazon S3 (Preview). This is reinforced by enhanced Lakehouse Governance (Preview), which ensures your security policies and access controls apply instantly across this borderless environment.
Unlocking operational data: We’re announcing Spanner Omni (Preview), unchaining the most scalable, globally consistent database on the planet. For the first time, you can run the Spanner engine anywhere — across clouds, on-premises, or on your laptop — with the same capabilities used to run Google.
Bridging the insight to action gap: We’re also closing the gap between insight and action. Most "unified" data platforms force the creation of complex ETL pipelines that block agents from accessing your real-time data. With Lakehouse federation for AlloyDB (Preview), we’re removing these pipelines by providing protocol-level, zero-ETL synchronization to give agents access to deep analytical history with low latency in operational transactions.

Automating the future

Moving to agent scale generates orders of magnitude more workloads. To support this, we are announcing four major performance breakthroughs:

Lightning Engine for Apache Spark delivers up to 2x the price-performance over the proprietary market alternative.
Managed Lustre delivers up to 10 terabytes-per-second of throughput to make sure data moves quickly enough for demanding models.
Bigtable now supports an in-memory tier that delivers sub-millisecond read latency for real-time applications. This means you can finally eliminate separate, side-by-side caching layers.
BigQuery fluid scaling helps lower costs by up to 34% on average for autoscaling workloads, scaling up resources instantly when agents act, and scaling back when they don't.

Build your success on a System of Action

The era of passive observation is over. The future belongs to the System of Action, made possible by Google’s Agentic Data Cloud.

Ready to see what an Agentic Data Cloud can do for your business?
Sign up for a strategy workshop today on how to get your data ready to fuel autonomous agents through a System of Action.

Want to learn more about an Agentic Data Cloud?
Read our blueprint for turning passive data into proactive action.

Introducing the Google Cloud Knowledge Catalog

Wed, 22 Apr 2026 12:00:00 +0000

Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack business semantics and data relationships, this triggers hallucinations, high latency, and stale insights.

To address this problem, we are evolving Dataplex into a dynamic, always-on Knowledge Catalog that serves as the universal context engine for your enterprise, helping agents execute complex tasks with accuracy.

Customers like Bloomberg Media are already using the Knowledge Catalog to power agents with trusted context:

“By unifying Bloomberg Media’s enterprise metadata and business context through the Knowledge Catalog, we successfully launched our Data Access AI Agent. This internal solution empowers stakeholders across the organization to intuitively explore our data lake, translating complex business inquiries into instant, AI-driven narratives. Crucially, by grounding our AI in trusted institutional context, we ensure confidence in the accuracy and quality of every insight generated."

— William Anderson, CTO, Bloomberg Media

The Knowledge Catalog operates on three foundational pillars:

Aggregation: Unifying context and resolving conflicting definitions
Enrichment: Generating continuous meaning and mapping relationships
Search: Empowering agents with high-precision retrieval

Aggregation: Unifying context across your data estate

To build true context, you must bring it together from everywhere. The Knowledge Catalog aggregates native context across your Google and partner data platforms, semantic models, and third-party catalogs, unifying them into a single, governed source of truth.

Broad metadata aggregation (GA): To build a truly comprehensive context engine, you must leave no silo behind. The Knowledge Catalog automatically harvests technical metadata across your foundational systems — including BigQuery, AlloyDB, Spanner, Cloud SQL, Firestore (Preview), and Looker (Preview). It also supports integrations with third-party databases and partner catalogs like Atlan, Collibra, Datahub, Ab Initio, and Anomalo, ensuring that even your legacy metadata is brought into the agentic fold.
Enterprise connectivity (Preview): To truly understand your operations, semantic context must cover all the key systems in your enterprise. Using Google Cloud Lakehouse, we are interconnecting these systems with context federation, and Knowledge Catalog gains full and immediate visibility to applications, operating systems, and AI platforms — including Palantir, Salesforce Data360, SAP, ServiceNow, and Workday. For example, your SAP data products are automatically mapped to the Knowledge Catalog.
BigQuery measures (Preview): We are redefining data consistency by embedding programmatic business logic directly into the SQL engine. BigQuery measures ensure every calculation is universally reusable and mathematically accurate. The Knowledge Catalog acts as the ultimate aggregator, pulling BigQuery measures and LookML together into a single, governed semantic foundation.
Data products (GA): Data products package data assets and context that grounds agents and makes them reliable in production. These self-contained blocks include built-in intent, SLAs, and governance constraints, providing the essential building blocks to scale complex AI use cases.

Enrichment: Generating meaning through continuous learning

The Knowledge Catalog provides continuous data enrichment — going beyond manual curation to actively mine structured schemas, query logs, and BI semantic models while extracting entity relationships from unstructured data. We are delivering this continuous enrichment where your teams work:

Smart Storage and Object Context API (Preview): Built natively into Google Cloud Storage (GCS), Smart Storage automatically tags, embeds, and enriches files with metadata as soon as they land in your buckets. By integrating this intelligence feature with the Knowledge Catalog, unstructured data is instantly discoverable by agents.
Deep multimodal metadata extraction (Preview): For collections of complex unstructured data, the Knowledge Catalog natively integrates with Gemini to identify useful business information and automatically build pipelines that extract entities and map complex business relationships directly from unstructured content.
Automated context curation (Preview): The Knowledge Catalog automatically generates natural language descriptions, including business glossaries, for datasets, data products, relationships, and verified SQL patterns that allow both humans and agents to interact with data without guesswork. By inferring these hidden relationships and intent-based patterns, it constructs a dynamic, evolving map of how data actually relates to the business.
Verified queries and semantic guardrails (Preview): One leading cause of AI failure is hallucinated logic and guessed SQL joins. To prevent this, the catalog provides verified SQL patterns and pre-generated natural language questions.

Search: Unleashing agents with high-precision, secure retrieval

Creating a massive context layer is great, but in the agentic era, search has evolved to be the new query path. When autonomous agents are working on your behalf, they are iterating incredibly fast. The hardest problems at enterprise scale are speed, relevance, global reach, and security.

High-precision semantic search (GA): The Knowledge Catalog uses a hybrid search stack leveraging decades of Google innovation. Built on the same advanced query-rewriting and machine-learning technologies that power Google Search, it delivers the sub-second latency and pinpoint relevance that agents need. When an agent receives a prompt, the catalog instantly ranks and returns the right context to agents in real-time.
Access control-aware search: The ability to find the right data and its corresponding context is critical in the agentic era; if an agent retrieves the wrong context, it hallucinates. To gain trust, our global search respects metadata access permissions as defined in the source systems, ensuring agents can only retrieve and act on the assets they are explicitly authorized to see.
Measurable context evaluation: To ensure long-term accuracy, we are augmenting our search capabilities with a robust evaluation framework. This transforms context construction from a guessing game into a measurable engineering discipline. It allows your teams to quantitatively test and iterate on various context construction strategies, ensuring continuous optimization of the relevance and quality of the context feeding your agents.

With foundational data products, high-precision search, and guardrails in place, we can deploy advanced AI reliably. A prime example is the Deep Research Agent in Gemini Enterprise, powered by the Knowledge Catalog (Preview). Now natively powered by the Knowledge Catalog, this agent synthesizes live business data, internal documents, and web research to answer highly complex questions. It delivers deterministic precision and deep citations, executing tasks in minutes that previously required weeks of manual effort.

Stop forcing your agents to guess the unwritten rules of your business. Build the context once, and unleash your agents to do the rest.

Get started today with Knowledge Catalog.

Go from blank slate to analysis with BigQuery Studio notebook gallery templates

Thu, 16 Apr 2026 16:00:00 +0000

For many data professionals, the most daunting part of a new project isn't the complexity of the data or the sophistication of the model, it’s the "blank slate." Staring at an empty notebook while creating a data cleaning pipeline from scratch, or searching for the best way to run time-series forecasting can stall your momentum before the real analysis even begins.

Today, we’re excited to announce the general availability (GA) of the BigQuery Studio notebook gallery. This curated collection of pre-built templates is designed to help you bypass the setup phase and jump straight into discovery.

Quickly browse through the notebook gallery and launch a template with a single click.

Move past the blank slate with curated templates

BigQuery Studio notebooks bring the power of Colab Enterprise directly into the BigQuery UI, providing a smooth transition between SQL-based data prep, Spark-powered processing, and Python-based analysis. The notebook gallery supports this unified experience with templates tailored to different skill sets and objectives.

For SQL developers: Many users are comfortable with SQL but want to explore the expanded capabilities of a notebook environment. Templates in the gallery demonstrate how to use SQL cells to load data and then use visualization cells for no-code charts, making it easier to share insights without writing extensive Python.
For data scientists: Python and Spark users can find ready-to-use workflows for data cleaning, transformation, and advanced ML development using BigQuery DataFrames and Spark libraries. These templates follow best practices, so that your code stays efficient and takes full advantage of BigQuery's distributed engine.

Choose the right template for your project

The gallery is organized to help you find the right starting point for your specific goals, from data analysis and visualization to building advanced data science workflows.

If you're starting your journey with BigQuery Studio notebooks, the gallery offers several introductory templates:

Introduction to notebooks in BigQuery Studio: A high-level tour that covers SQL cells, visualization cells, Python-based visualizations, and running AI predictions.
Getting started for SQL users: A guide for those comfortable with SQL who want to make queries dynamic with Python variables and visualize findings, without writing complex code.
Getting started for Python users: A workflow focused on using BigQuery DataFrames to clean, merge, and analyze datasets.
Getting started with Spark: A hands-on guide to launching serverless Apache Spark sessions in BigQuery Studio to join, analyze, and visualize BigQuery data using Spark SQL and Python.

For seasoned notebook users, you can leverage specialized templates to handle complex analytical workflows:

Generative AI & Multimodal Analysis: Unify structured and unstructured data. Use the Analyze multimodal data in BigQuery template to apply Gemini models to images or audio files and return insights directly as SQL results.
Machine Learning Development: Accelerate the ML lifecycle with the Exploratory data analysis with BigQuery DataFrames template, which uses BigFrames to perform feature engineering and model training at scale. For distributed workloads, the E-commerce purchase predictions with Apache Spark ML template provides a complete, serverless workflow for training predictive models on BigQuery data.
Data Pipelines & Transformation: Master data reliability and real-time streaming. Use the Data Quality and Profiling with BigFrames template for cleaning datasets, or the Real Time Data Export to Pub/Sub template for operationalizing data with continuous queries.

The introduction to notebooks template is a great one to open for any new project as it covers the major features of BigQuery Studio notebooks.

Access the gallery in your workflow

You can find the gallery directly in the BigQuery Studio console:

1. From the welcome page: Navigate to the "Welcome to BigQuery Studio" page and click View notebook gallery.

2. From the asset menu: Click the (+) icon to create a new asset, select Notebook, and then choose All templates.

The gallery allows you to filter by task, such as data transformation or predictive analysis, so you can find the specific workflow that matches your goals. When you find the right template, you can open a read-only version to preview it and then click a button to add a copy to your project.

Get started

Open the notebook gallery in BigQuery Studio today to find a template for your next project.

Open the gallery: Explore the curated collection in the BigQuery console.
Read the documentation: Learn more about how to use these templates in our official guide.

Cool stuff Google Cloud customers built, April edition: BMW big on SLMs, MLB’s Scout Insights AI, personalized resort experiences

Wed, 15 Apr 2026 16:00:00 +0000

AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, who are building the future on our platform, there would be no Google Cloud. In this regular round-up, we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories.

For our latest edition, we learn why BMW Group is experimenting with small language models (SLMs); catch AI-powered commentary from Major League Baseball; hit the slopes with Vail Resort’s AI concierge; build an intelligent grid with CTC Global; witness how ID.me created secure global scale; and see how Manhattan Associates supply chain tools now handle 1 billion daily API calls.

Be sure to check back next month to see how more industry leaders and exciting startups are putting Google Cloud technologies to use. And if you haven’t already, please peruse our list of 1,001 real-world gen AI use cases from our customers.

BMW tests the big potential of small models

Who: As one of the world’s leading providers of premium cars and motorcycles, BMW Group is always at the forefront of automotive technology. This ethos pushed the company to test what type of AI language models are ideally suited to driving situations, where access to cloud-based LLMs isn’t always possible.

What they did: BMW Group wanted to explore the potential of small language models (SLMs), which could run within the limited hardware on a vehicle. Finding the right trade-off between size and capability requires careful optimization, and the sheer volume of viable combinations renders manual searches for the optimal configuration an incredibly tedious, if not impossible, undertaking. To overcome this challenge, BMW and Google built automated, reproducible workflows through executable pipelines using Vertex AI.

Why it matters: The path from a general-purpose LLM to a specialized SLM isn’t straightforward. Every choice — from type of quantization to characteristics and contents of the fine-tuning domain-specific dataset — affects the quality and efficiency of the final model. This creates an exponential range of configurations, each with different trade-offs. It’s a great example of using AI to scale an optimization problem for other AI.

Learn from us: “With automated pipelines, we can rapidly adapt models to our domain and rigorously test and evaluate them against domain-specific benchmarks. This allows us to iterate and optimize models in hours rather than days.” – Dr. Céline Laurent-Winter, vice president, Connected Vehicle Platforms at BMW Group

MLB Scout Insights: AI-powered color commentary

Who: Major League Baseball is famous for its colorful announcers. Now, MLB is bringing more baseball color straight to your pocket, and Gemini is helping give it a voice.

What they did: Each season, millions of baseball fans use the MLB app and tap over to the Gameday feature for live, up-to-the-pitch action across more than a dozen games. Starting this season, the league launched MLB Scout Insights in Gameday, which uses Gemini models to quickly scan decades of game and player data, cross-references it with situational game scenarios, and then delivers game-relevant context during key matchups.

Why it matters: Given the sport’s storied history, 162-game regular season, and global reach, baseball fans are among the most sophisticated and passionate out there. To keep them engaged with Gameday and the MLB app, the league wanted to deliver insights that truly felt meaningful and interesting. Building the tool meant answering a rather squishy question: What makes an insight actually insightful, not just an accurate fact, and how can an AI learn that distinction? The answer came from some clever “surprisal” analysis.

Learn from us: "With Scout Insights, every fan can feel like the smartest person in the stands, at the water cooler, or on the couch. It’s about deepening connections to the game, and sharing that passion with others. That’s the magic of sports, and we’re making more of it possible with the magic of AI." – Josh Frost, senior vice president of product & Matt Graser, director of engineering, Major League Baseball

Vail Resorts makes personalized AI assistance easy

Who: Vail Resorts operates some of the most iconic and beloved mountain destinations in the world, including Whistler Blackcomb, Park City Mountain, Stowe, and Crested Butte.

What they did: Vail Resorts launched My Epic Assistant during the 2024-2025 snow season, and expanded it this year to add even more AI-powered chat features powered by Google’s powerful Gemini models. The result is an agentic guide to the slopes that can help skiers and snowboarders decide on the right season pass, share the latest snow report, check on lesson preparations, or suggest a good stop for cocoa.

Why it matters: Vail Resorts wanted more than a chatbot; they wanted a digital concierge that understands the nuance between a powder day at Whistler and a family trip to Beaver Creek. As the company implemented and refined personalization, improved search, summary capabilities, and conversational flow within My Epic Assistant, the app has delivered a 45% reduction in escalation to human agents since launch.

Learn from us: "Utilizing tooling from Google Cloud, we could lean into agentic design patterns that gave us a way to unlock natural, personalized conversations. These boosted customer satisfaction, while reducing the need for manual intent design. These tools also let us combine flexibility and control to enable the assistant to respond fluidly but always within the boundaries of our brand, policies, and product strategy.”— The Vail Resorts technical team

CTC Global turns the smart grid into an intelligent one

Who: CTC Global is a leading manufacturer of advanced transmission conductors and power lines. While many nodes in the grid contain IoT sensors, it recognized a literal gap in the transmission lines themselves.

What they did: CTC’s new GridVista platform threads fiber-optic cable into its high-strength carbon fiber composite core, and connects these to monitoring technology built with AI and monitoring technology from Google Cloud and Tapestry. With GridVista, CTC can turn every inch of transmission into a smart sensor.

Why it matters: GridVista gives CTC grid operators an accurate and reliable view of what’s happening across the entire line — based on actual, real-time data from the entire length of the conductor, not point estimates from a static model of line conditions or the occasional clamped-on sensor. This means they can significantly improve safety, manage costs, increase the line’s capacity to transmit power, and enhance reliability with more precise insights about events that might trigger an outage.

Learn from us: “This awareness allows for a grid that can truly sense its own health in real time and provide unprecedented awareness of conditions on the entire line. Whether that’s real time storm impacts, ice load, wind load, branches on the wire, or temperatures on or under the line. The GridVista system truly represents next generation capabilities. ” — J.D. Sitton, CEO, CTC Global

ID.me reduces risk while scaling past 160 million users

Who: ID.me is transforming digital identity security for the modern era, offering a single login that lets you easily prove you’re you across a wide range of platforms and wallets.

What they did: ID.me currently serves more than 160 million users, including as many as 40,000 at any time, so they can prove their identity online as easily as flashing their driver’s license in person. Over the last two years, ID.me migrated more than 50 terabytes of data across 15 database instances to Google Cloud with minimal downtime. They also introduced a two-tier architecture with Cloud SQL supporting its smaller and more standard services, while AlloyDB runs heavier workflows that form the backbone of the ID.me platform.

Why it matters: AlloyDB AI has allowed ID.me to scale its systems to handle 10X-20X of what was possible before — and at a lower price to boot. That responsiveness and reliability led the U.S. federal government to recognize ID.me for its role in preventing large-scale fraud within national systems.

Learn from us: "We’ve been able to scale both our infrastructure and trust. With a platform that’s faster, smarter, and built to handle portable identity at massive scale, we’re one step closer to our goal: a secure, digital way to prove who you are, wherever you need it, that works everywhere you need it." — Kevin Liu, Cloud Platform Architect, ID.me

Manhattan Associates powers more than a billion daily API calls

Who: Manhattan Associates is a global leader in supply chain and omnichannel commerce solutions, offering tools and platforms that reach more than 2 billion people across 20 billion consumer touchpoints.

What they did: Manhattan Associates modernized its Manhattan Active SaaS platform by migrating from legacy Oracle and DB2 systems to Google Cloud databases. Each capability of Manhattan Active now runs as an independent, containerized service orchestrated by Google Kubernetes Engine (GKE). Data flows through Pub/Sub into BigQuery for real-time analytics, while Cloud Logging and Cloud Monitoring deliver observability at scale.

Why it matters: With its new microservices-first design, Manhattan gained the agility to evolve faster and the confidence that mission-critical operations would remain resilient across regions. With Cloud SQL and BigQuery, the company now processes more than a billion daily API calls with average response times of less than 150 milliseconds. This evolution supports hundreds of thousands of monthly active users across tens of thousands of stores and distribution centers. The new platform also created the foundation for Manhattan’s Agentic AI suite, which includes prebuilt agents — like the Intelligent Store Manager and Labor Optimizer — that coordinate real-time decisions across store and distribution center operations.

Learn from us: "Operationally, the platform has become more elastic and efficient. The system automatically handles hundreds of thousands of scaling events per day, ensuring performance remains consistent during peak surges without expensive overprovisioning." — Narayana Reddy Kothapu, Senior Director, Manhattan Associates & Rajkumar Ramani, Technical Director, Manhattan Associates

Introducing BigQuery Graph: Unlock hidden relationships in your data

Tue, 14 Apr 2026 16:00:00 +0000

Today, we're thrilled to announce that BigQuery Graph is now available in preview. With BigQuery Graph, we’ve built an easy-to-use, highly scalable graph analytics solution for data engineers, data analysts, data scientists, and AI developers, empowering them to model, analyze and visualize massive-scale relationships in an entirely new way.

As data changes and grows, it’s important to understand how different entities such as people, places, and products relate to one another. After all, data is more meaningful when we know how entities are interconnected. With traditional SQL, to find a "friend of a friend of a friend" requires multiple nested JOIN operations, which are usually challenging to read and write, and the performance degrades exponentially at scale. Finding the "blast radius" of a supply chain disruption during a storm requires multi-hop traversals, a full-scale graph analysis. To better solve this challenge, data is often modeled as a graph representation of the physical world around us, which can be better at finding complex and hidden relationships than traditional relational data structures.

Graph challenges faced by enterprises across industries

Graph technology has been broadly used across industries for fraud detection, recommendation engines, supply chain management, knowledge graph applications, and many others. However, users face some major challenges in adopting graphs:

Data silos and maintenance overhead: Having to store and maintain graph data in a standalone graph database creates data silos, data inconsistency, additional cost — not to mention extra operational overhead.
Lack of graph expertise: Adopting graph technologies often requires learning a new language, paradigm, and potentially a new database. At the same time, organizations’ investment in SQL expertise are less relevant.
Performance and scalability concerns: Many standalone graph databases work well when traversing the graph from a handful of nodes, but struggle to scale to billions of entities as business demands grow.

BigQuery Graph addresses many of these challenges by supporting:

Built-in graph query experience: A more intuitive graph query language (GQL) allows you to find patterns and traverse relationships among disparate data sets, based on the newest ISO GQL standard.
Unified relational and graph data models: Tight integration between graph and relational data models allows you to choose the best tool to model the data on a single source of truth without data duplication or data movement. Full interoperability between graph queries and SQL allows you to continue to leverage existing SQL skills, while taking advantage of the expressiveness of graph queries.
Graph over structured and unstructured data: Rich AI functions, vector and full-text search capabilities are supported with BigQuery Graph, allowing you to use semantic meaning, keyword search on graphs, bridging the gaps of structured and unstructured data.
Graph visualization: You can easily explore, investigate, and explain how your data is connected in an intuitive graph format using BigQuery Studio notebook and Jupyter Notebook.
Industry-leading ease of use, performance and scalability: BigQuery Graph is built upon BigQuery's serverless, scalable, cost-effective and distributed analytics engine that can scale to billions of nodes and edges.
Integration with Spanner Graph: This provides a unified graph schema and graph query language that serve a full spectrum of real-time (Spanner Graph) and batch graph needs (BigQuery Graph). You can also build a virtual graph by combining the latest data from Spanner and historical data from BigQuery without data movement using federated queries.
Chat with your graph: Very soon, you will be able to chat directly with graphs with the Conversational Analytics Agent (stay tuned).

Common BigQuery Graph use cases

BigQuery Graph opens up a realm of possibilities across industries for building intelligent applications:

Financial fraud detection: Analyze complex relationships among users, accounts, and transactions to identify suspicious patterns and anomalies, such as money laundering and irregular connections between entities, which can be difficult to detect using relational databases.
Customer 360: Track customer relationships, preferences, and purchase histories. Gain a holistic understanding of each customer, enable personalized recommendations, targeted marketing campaigns, and improved customer service experiences.
Social networks: Capture user activities and interactions and use graph pattern matching for friend recommendations and content discovery.
Manufacturing and supply chain management: Use graph patterns for efficient stockout analysis, cost rollups, and compliance checks by modeling parts, suppliers, orders, availability, and defects in the graph.
Healthcare: Capture patient relationships, conditions, diagnosis, and treatments to facilitate patient similarity analysis and treatment planning.
Transportation optimization: Model places, connections, distances, and costs in the graph, and then use graph queries to find the optimal route.

BigQuery Graph in the real-world

Many customers across industries have leveraged BigQuery Graph to solve real-world business challenges. Here are a few examples of how they are putting these capabilities into practice:

BioCorteX: drug discovery

"Understanding disease isn't about collecting more data; it’s about understanding the relationships within that data. By leveraging pathway search in BigQuery Graph at a massive scale, reaching depths of 7+ hops, we are finally able to see more of the human metabolism map. This level of scale is what allows us to move beyond trial and error, identifying the precise biological levers that need to be pulled to cure complex diseases. We aren't just guessing anymore, we’re emulating life at the speed of compute." - Nik Sharma, CEO and Cofounder, BioCorteX

Curve: fraud detection

"By implementing BigQuery Graph, we have successfully moved away from the previous limited sql-based approach to a more scalable solution for fraud detection network analysis. This has allowed us to detect sophisticated fraud networks by uncovering hidden connections between seemingly unrelated accounts and transactions. This transition from traditional relational queries to graph-based analytics has showcased measurable business impact with ~£9.1M of savings. This shift has not only improved the precision of fraud detection but has also provided a scalable foundation for protecting the ecosystem without adding significant operational overhead." - Francis Darby, VP Data & ML, Curve

Virgin Media 02: fraud detection

"At Virgin Media O2, we are constantly evolving our defenses to stay ahead of increasingly sophisticated fraud networks. We’ve added a powerful new layer to our already robust fraud alerting system. Using BigQuery Graph, we can now execute complex 4-hop queries that map the hidden relationships between accounts, devices, and activities. This deeper visibility allowed us to identify networks of suspicious addresses. This doesn't just catch fraud; it acts as an early warning system, flagging new connections to known risk networks before they can cause damage." -- Jonathon Ford, Director Data Applications, Virgin Media O2

How to use BigQuery Graph

BigQuery Graph is more than just a new feature; it's a new way of thinking about data, empowering you to ask bigger questions, uncover deeper insights, and solve your most challenging problems.

You can get started in three simple steps:

Step 1: Create graph schemas on top of the relationship tables using DDL with a single copy of data.

Create a finance graph by mapping relational tables into “Account”, “Person”, "Loan" nodes and their relationships ��Transfers”, “Owns”, "Repays" via edges.

code_block: <ListValue: [StructValue([('code', 'CREATE PROPERTY GRAPH graph_db.FinGraph\r\nNODE TABLES (\r\n graph_db.Account KEY(id),\r\n graph_db.Person KEY(id),\r\n graph_db.Loan KEY(id)\r\n) \r\nEDGE TABLES (\r\n graph_db.Transfers \r\n KEY (id, to_id, timestamp) \r\n SOURCE KEY (id) REFERENCES Account (id)\r\n DESTINATION KEY (to_id) REFERENCES Account (id), \r\n graph_db.Owns\r\n KEY (id, account_id, timestamp) \r\n SOURCE KEY (id) REFERENCES Person (id)\r\n DESTINATION KEY (account_id) REFERENCES Account(id),\r\n graph_db.Repays\r\n KEY (id, loan_id, timestamp) \r\n SOURCE KEY (id) REFERENCES Person (id)\r\n DESTINATION KEY (loan_id) REFERENCES Loan(id)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d935f6340>)])]>

Step 2: Use intuitive SQL/GQL to traverse data relationships and find hidden connectivities.

Find the accounts owned by Jacob and the loans he repays from those accounts:

code_block: <ListValue: [StructValue([('code', 'GRAPH graph_db.FinGraph\r\nMATCH\r\n (person:Person {name: "Jacob"}) \r\n -[own:Owns]->(account:Account)\r\n -[repay:Repays]->(loan:Loan)\r\nRETURN\r\n account.id AS account_id,\r\n loan.id AS loan_id'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d935f6d30>)])]>

Combine vector search with graph traversals to find fraudster-like accounts and their transfer activities within 1-6 hops:

code_block: <ListValue: [StructValue([('code', "DECLARE similar_account_to_fraudster DEFAULT ((\r\n SELECT array_agg(base.id)\r\n FROM VECTOR_SEARCH(TABLE graph_db.Account, 'embedding',\r\n (SELECT * FROM graph_db.Account WHERE id=102), 'embedding', \r\n top_k => 6)\r\n));\r\nGRAPH graph_db.FinGraph\r\nMATCH\r\n (person:Person)-[own:Owns]->\r\n (account:Account)-[transfer:Transfers]->{1,6}\r\n (to_account:Account)\r\nWHERE to_account.id IN \r\n UNNEST(similar_account_to_fraudster)\r\nRETURN\r\n person.id AS person_id,\r\n account.id AS src_account,\r\n to_account.id AS to_account"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d935f60d0>)])]>

Step 3: Visualize graph results to detect connectivity of disparate data in a more intuitive way in BigQuery Studio notebook.

If you are looking for a specialized graph visualization tool, BigQuery Graph has integrated with industry leading partners including G.V(), Graphistry, Kineviz, Linkurious. They allow you to see a visualization of BigQuery Graph query results outside the Google Cloud console.

Ready to get started?

The future of data analysis is connected. With BigQuery Graph, you have the power to unlock that connectivity and transform your business into actionable insights grounded with your enterprise knowledge. Start exploring today and unleash the power of your data's interconnected relationships!

Visit the BigQuery documentation: find overview and quickstart guide.
Explore tutorials: get hands-on experience with BigQuery Graph through tutorials.
Share your feedback: join our community, and get your questions answered via bq-graph-preview-support@google.com.
Related blogs:

From operational to analytical: The unified Spanner Graph and BigQuery Graph solution

Tue, 14 Apr 2026 16:00:00 +0000

Understanding the relationships within your data is crucial for uncovering hidden insights and building intelligent applications. However, managing operational (OLTP) and analytical (OLAP) graph workloads usually means wrestling with disconnected databases, building brittle data pipelines, and managing complex integrations. This fragmentation creates data silos, increases operational overhead, and limits scalability.

Today, we're thrilled to introduce a unified graph database and analytics solution powered by Spanner Graph and BigQuery Graph. The solution consists of the two platforms, recommended blueprints for how to deploy them, and getting started guides for the most prominent use cases. In this blog, we review the solution’s components, provide an overview of the most common use cases, and hear from customers who have deployed the solution in the real world.

Spanner Graph for operational workloads

Spanner Graph reimagines graph data management, bringing together graph, relational, search, and generative AI capabilities into a single database. It is backed by Spanner’s signature unlimited scalability, high availability, and strong consistency.

With Spanner Graph, you get:

Integrated table-to-graph mapping: Define graphs directly over your existing Spanner relational tables, allowing you to view and query operational data as a graph without data duplication.
Interoperable graph and relational querying: Leverage an ISO-standard Graph Query Language (GQL) interface for intuitive pattern matching, and mix GQL with SQL in a single query to traverse both graph and tabular data together.
Advanced search and AI integration: Utilize built-in vector search, full-text search, and Vertex AI integration to retrieve data by semantic meaning and power intelligent applications directly within your database.

Customers are already using Spanner Graph to power high-throughput, low-latency applications - for identity resolution across millions of entities, identifying dependencies across vast complex environments, data lineage, customer 360 use-cases, and enhancing real-time fraud detection.

"Open Intelligence is our foundational intelligence layer that securely connects trillions of live data points from clients, partners and WPP in a privacy-first way and is now integrated and powers WPP’s agentic marketing platform, WPP Open. Enabled by Google Cloud's Spanner Graph, Open Intelligence is a significant advancement in AI-driven marketing and we are excited about extending the use case for analytical graph workloads on BigQuery Graph." - Rob Marshall, Head of Strategy, Data & Intelligence, WPP

BigQuery Graph for analytical workloads

While Spanner Graph handles your active operations, true large-scale analysis requires exploring relationships across billions of nodes and edges to identify patterns and query historical data. Just as SQL relies on distinct tools for databases and data warehouses, the graph landscape requires specialized tools for different workloads. That's why we built BigQuery Graph.

BigQuery Graph brings connected data analytics directly into your data warehouse. You can map existing BigQuery data to a graph schema and query it with SQL or GQL to uncover hidden relationships in massive datasets - without moving any data.

Key capabilities include:

Integrated table-to-graph mapping: Map your existing BigQuery tables to graphs instantly, uncovering hidden relationships in your data warehouse without building ETL pipelines or moving a single byte of data.
Interoperable graph and relational querying: Apply the same expressive pattern matching of GQL to massive historical datasets, and mix SQL with GQL in a single query to combine the familiarity of your data warehouse with powerful graph traversal.
Advanced search and AI integration: Leverage native integration with BigQuery AI for predictive analytics, alongside built-in vector search, full-text search, and geospatial functions to locate connected information across billions of records.

Spanner Graph and BigQuery Graph as a unified solution

While each platform is powerful on its own, their true value emerges when they are deployed together. By connecting your operational and analytical environments, you eliminate data silos and accelerate your time-to-insight without compromising database performance.

"Spanner Graph enables Yahoo to unify our data into a connected foundation at a global scale, powering real-time, intelligent decision-making across our agentic advertising platform. This enhances our AI-driven approaches that drive one of the largest digital advertising ecosystems, and we look forward to building on it with BigQuery Graph to unlock deeper analytics and predictive capabilities to power future innovation." - Gabriel DeWitt, Head of Consumer Monetization, Yahoo

Take financial fraud detection as an example: your application can use Spanner Graph to instantly identify a suspicious connection and block a transaction at checkout. Meanwhile, BigQuery Graph can analyze petabytes of historical transaction data to expose the complex, long-term fraud ring that initiated it.

Here is how these two engines integrate to create an end-to-end graph workflow:

1) A unified graph query and schema experience

A core advantage of this solution is the consistent schema and GQL shared across both platforms. This shared language reduces development time and minimizes the friction of context-switching.

For example, to find potential fraud rings originating from a specific account in real-time, you would use this Spanner Graph query:

code_block: <ListValue: [StructValue([('code', 'GRAPH FinGraph\r\nMATCH p=(:Account {id: @accountId})-[:Transfers]->{2,5}(:Account)\r\nRETURN PATH_LENGTH(p) AS path_length, TO_JSON(p) AS full_path;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d911a65e0>)])]>

To run that same analysis to find all accounts involved in historical fraud rings, the BigQuery Graph query is nearly identical:

code_block: <ListValue: [StructValue([('code', 'GRAPH bigquery.FinGraph\r\nMATCH p=(:Account)-[:Transfers]->{2,5}(:Account)\r\nRETURN PATH_LENGTH(p) AS path_length, TO_JSON(p) AS full_path;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d911a6eb0>)])]>

2) Query Spanner data in BigQuery Graph through Data Boost

With Data Boost, you can query Spanner Graph data directly from BigQuery without impacting performance of your transactional workloads. This allows you to build a "virtual graph" combining real-time operational data from Spanner with historical analytics in BigQuery - without moving any data.

For instance, you can combine real-time Account and User nodes from Spanner Graph with historical LogIn edges from BigQuery to identify suspicious login patterns across different devices.

To do this, you first connect BigQuery to Spanner using the CREATE EXTERNAL SCHEMA statement:

code_block: <ListValue: [StructValue([('code', "CREATE EXTERNAL SCHEMA spanner\r\nOPTIONS (\r\n external_source = 'google-cloudspanner:/projects/PROJECT_ID/instances/INSTANCE/databases/DATABASE',\r\n location = 'LOCATION'\r\n);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d911a6070>)])]>

Next, define a BigQuery Graph, incorporating tables from both Spanner and BigQuery:

code_block: <ListValue: [StructValue([('code', 'CREATE OR REPLACE PROPERTY GRAPH bigquery.FinGraph\r\n NODE TABLES (\r\n -- Account and Person are stored in Spanner,\r\n -- made available in BigQuery through the `CREATE EXTERNAL SCHEMA` statement.\r\n spanner.Account KEY (account_id),\r\n spanner.Person KEY (person_id),\r\n -- Media is stored in BigQuery.\r\n bigquery.Media KEY (media_id)\r\n )\r\n EDGE TABLES (\r\n -- Transfers and Owns are stored in Spanner.\r\n spanner.Transfers AS Transfers\r\n KEY (transfer_id)\r\n SOURCE KEY(account_id) REFERENCES Account\r\n DESTINATION KEY(target_account_id) REFERENCES Account,\r\n spanner.Owns AS Owns\r\n KEY (person_id, account_id)\r\n SOURCE KEY(person_id) REFERENCES Person\r\n DESTINATION KEY(account_id) REFERENCES Account,\r\n -- LogIn is stored in BigQuery.\r\n bigquery.LogIn AS LogIn\r\n KEY (login_id)\r\n SOURCE KEY(media_id) REFERENCES Media\r\n DESTINATION KEY(account_id) REFERENCES Account,\r\n );'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d911a6640>)])]>

Finally, execute a query on BigQuery Graph to access data across both Spanner (accounts, users, transfers, owns) and BigQuery (logins, devices), identifying potentially suspicious login patterns:

code_block: <ListValue: [StructValue([('code', 'GRAPH bigquery.FinGraph\r\nMATCH p=(owner:Person)-[:Owns]->\r\n (:Account)<-[login:LogIn]-\r\n (media:Media {blocked: true})\r\nRETURN TO_JSON(p) AS full_path\r\nORDER BY login.time\r\nLIMIT 20;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d93880220>)])]>

3) Export BigQuery data into Spanner Graph through reverse ETL

When you need to bring analytical data back into Spanner for low-latency, real-time querying, you can use reverse ETL without additional pipelines. For example, you can import historical device data (IP addresses, device IDs) from BigQuery into Spanner Graph to enhance your real-time fraud detection operations:

code_block: <ListValue: [StructValue([('code', 'EXPORT DATA\r\n OPTIONS (\r\n uri = \'https://spanner.googleapis.com/projects/PROJECT_ID/instances/INSTANCE/databases/DATABASE\',\r\n format=\'CLOUD_SPANNER\',\r\n spanner_options="""{ "table": "Media" }"""\r\n ) AS \r\nSELECT * FROM Media;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d93880280>)])]>

4) Visualize your graph data

Visualizing connected data is core to analysis, explorations and investigations. With Spanner Studio and BigQuery Studio (coming soon), you can instantly visualize your graph data without leaving your familiar environment or setting up external tools. For deeper programmatic exploration, you can also leverage Spanner Graph notebook and BigQuery notebooks to render query results directly within your existing data science workflows.

5) Graph visualization partner integrations

Spanner Graph and BigQuery Graph also integrate with leading graph visualization partners to provide a comprehensive suite of exploration tools:

Kineviz: Combines cutting-edge visualization with advanced analytics via GraphXR.
Graphistry: Extracts meaningful insights from large datasets using a GPU-accelerated visual graph intelligence platform.
G.V(): Offers a quick-to-install client for high-performance visualization and no-code data exploration.
Linkurious: Detects and analyzes threats in large volumes of connected data via the Linkurious Enterprise platform.

One unified solution for all your graph needs

Together, Spanner Graph and BigQuery Graph provide a unified solution for operational and analytical needs across various use cases:

Domains	Spanner Graph	BigQuery Graph
Financial Services	Instantly blocks anomalous, suspicious transactions.	Uncovers complex, long-term fraud rings.
Retail & E-commerce	Serves personalized product recommendations on the fly.	Analyzes vast purchasing histories to predict demand.
Cybersecurity	Isolates active threats and traces attack origins instantly.	Models historical vulnerabilities to strengthen defenses.
Healthcare	Powers clinical decision support systems at the point of care.	Analyzes population health trends and disease risk factors.
Supply Chain	Tracks goods globally and alerts teams to immediate disruptions.	Identifies systemic bottlenecks to optimize future routing.
Telecommunications	Creates a network digital twin for detecting anomalies, and root cause analysis in real-time.	Analyzes traffic patterns at scale to plan future infrastructure upgrades.

Get started with Spanner Graph and BigQuery Graph today

With Spanner Graph and BigQuery Graph, we’re excited to offer a unified graph data management experience across your operational and analytical needs.

Explore Spanner Graph's use cases and setup guide for your operational workloads, and the BigQuery Graph overview and creation guide for your analytical needs. To experience the full power of this combination, check out our unified solution guide and try the codelab.

Scaling unstructured enterprise knowledge with BigQuery Graph, and Kineviz GraphXR

Tue, 14 Apr 2026 16:00:00 +0000

Over 80% of enterprise data lives in unstructured form — PDFs, emails, reports, regulatory filings. Most of the time, such sources contain critical business information, yet they remain difficult to access and reason over at scale. Together, BigQuery Graph and Kineviz GraphXR give decision makers power over their unstructured data by creating a single, streamlined workflow that makes it much easier to uncover hidden business insights. BigQuery houses and builds the structures of the graph; Kineviz GraphXR lets analysts visually verify relationships, trace insights back to sources, and answer questions interactively.

Retrieval-augmented generation (RAG) and vector search have become the industry standard approach for working with unstructured data. When it comes to trend analysis, comparison across entities, multi-hop reasoning, and explainable decision support, graphs complement RAG by incorporating context and relationship mapping. Our "evidence-first" knowledge graph approach prioritizes preserving the nuance of the original evidence and maintaining the traceability of every single element in the graph, making the resulting analysis verifiable and trustworthy. In this post, we describe an example where BigQuery AI Functions, BigQuery Graph, and Kineviz GraphXR address business questions about Fortune 500 SEC filings without complex ETL pipeline, data duplication, or separate graph databases.

From fragmented to unified with BigQuery

Traditional unstructured analytics pipelines can be complex and sprawling. They typically involve multiple steps, including: object storage for raw files, a custom parsing service, a separate AI extraction layer, a standalone graph database, and finally, a BI tool for analysis. This complex setup can be difficult to maintain, involving data duplication, synchronization overhead, introducing multiple potential points of failure.

BigQuery streamlines this process. Raw documents are stored in Google Cloud Storage, and text extraction, Gemini-powered inference, and graph creation all run directly within the same platform. This removes the need for data movement between systems, complex service orchestration, or the accumulation of out-of-sync data copies.

With its tight integration, the pipeline is simple and maintainable, allowing full provenance without bespoke infrastructure.

BigQuery pipeline: From unstructured to structured

We used BigQuery pipeline to explore SEC 10-K filings of Fortune 500 companies from 2020 to 2024. Each filing includes around 100 pages of detailed, descriptive information.

We designed a schema such that each Company connects to Competitors (COMPETES_WITH), Risks (FACES_RISK), and Markets (ENTERING / EXITING / EXPANDING), and followed the following four-step process.

1. Ingest and parse. Retrieve 10-K filings from SEC EDGAR, transform Standard Generalized Markdown Language (SGML) to Markdown while preserving hierarchical structure, and load the raw text into BigQuery via Cloud Storage.

2. Focus on key signal sections. Instead of processing complete 100-page filings, we focus extraction only on sections related to market moves, risks, and competitors (specifically the Business, Risk Factors, and MD&A sections). Every row in BigQuery preserves essential metadata, including the year, company, CIK, section ID, and the direct URL to the original source filing.

3. Gemini for extraction. Utilizing AI.GENERATE_TEXT() with Gemini 3 Pro, each section is processed to return structured JSON. This output details competitors, risks, market actions, and opportunities, with every element grounded by evidence text from the initial filing. This process is completed entirely within BigQuery, with no external orchestration or data movement.

4. Declaring the graph. The structured JSON data is then broken down into distinct tables for nodes and edges. These tables are subsequently mapped into a fully traversable graph using a single Data Definition Language (DDL) statement, as shown below, enabling graph queries without the need for joins.

code_block: <ListValue: [StructValue([('code', 'CREATE PROPERTY GRAPH sec_filings.SecGraph\r\n NODE TABLES (\r\n nodes_company, nodes_competitor, nodes_risk, nodes_market, nodes_opportunity\r\n )\r\n EDGE TABLES (\r\n edges_competes SOURCE nodes_company DESTINATION nodes_competitor LABEL COMPETES_WITH,\r\n edges_faces_risk SOURCE nodes_company DESTINATION nodes_risk LABEL FACES_RISK,\r\n edges_entering SOURCE nodes_company DESTINATION nodes_market LABEL ENTERING\r\n -- plus EXITING, EXPANDING, PURSUING\r\n );'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f7d8c753bb0>)])]>

The process extracted 87,000 entities and over 20,000 mentions of competitors. After resolution and normalization, these mentions were consolidated into roughly 8,100 distinct competitors, turning unstructured SEC filings into a knowledge graph for competitive landscape.

Unlocking hidden insights with Kineviz GraphXR

GraphXR, by Kineviz, connects directly to BigQuery Graph, providing the environment for analysts to explore and analyze the data interactively. Analysts can visually navigate relationships and drill into subgraphs through low-code workflows, without needing to write any queries. This means strategy, compliance, and research teams can work directly with the data and refine their analyses themselves.

GraphXR’s AI-assisted workflows allow users to define analytical tasks using natural language, such as "show me Apple’s competitive trajectory over time", generating dashboards linked to a live graph view. As the graph view changes, dashboard charts update dynamically. For example, structured data points extracted from SEC filings reveal that the number of companies that cited Apple as a competitor has remained relatively stable at around 14 over time, a pattern not apparent when examining individual filings.

Dashboard: Companies Citing Apple Over Time

The AI-powered Visual Analysis Agent enhances the accuracy and nuance of these assessments. For instance, after using GraphXR's "trace neighbor" function to identify companies that cite Google as a competitor, the Agent's analysis reveals complex cross-industry relationships. A key example is AES Corp., an energy utility, which appears in contexts indicating coopetition relationships, illustrating the broader market shift toward adopting cloud and AI infrastructure.

Competitive analysis with agent reasons over both graph structure and node properties

Our workflow places a strong emphasis on auditability. Every node in the graph is directly linked to its source within the original SEC filings. Analysts can trace insights back to their origin and validate findings in context. For example, in the image below, selecting a risk entity provides a URL link that takes the reader to the relevant location in the document where that specific risk was identified.

Risk analysis with a direct, clickable link to the precise location of the extracted information in the source document.

Why this matters

BigQuery Graph together with Kineviz GraphXR provide organizations with:

Simplicity: Fewer systems, fewer copies — the pipeline runs in a fully-managed, integrated platform where data stored in BigQuery gets explored and analysed in GraphXR without data movement or duplication.
Scalability: BigQuery handles millions of documents and billions of extracted facts without bespoke graph infrastructure.
Explainability: Every insight traces back to source evidence; validation is one click.
Flexibility: New questions or entity types don't force you to rebuild the extraction model — you can just extend the schema.

The majority of enterprise knowledge is locked away. Together, BigQuery AI Functions, BigQuery Graph, and Kineviz BI tools provide an end-to-end solution that turns graph-based reasoning, evidence-first analytics, and interactive exploration into a single, streamlined pipeline that unlocks the intelligence trapped within unstructured data.

Get started

Learn more about BigQuery Graph here and get started here. Kineviz GraphXR is available on the Google Cloud Marketplace. You can find the Fortune 500 tutorial in the GitHub notebook or watch the video here.

Related blogs:

Accelerating data curation with Google Data Cloud

Fri, 10 Apr 2026 17:00:00 +0000

In the enterprise landscape, data is often highly fragmented across multiple source systems. Data curation is the process of organizing, cleaning, and enriching raw data to transform it into high-quality, AI-ready data assets. The traditional process of merging and cleaning this data using ETL tools, manual SQL or Python to build dashboards is the primary bottleneck for AI and analytics.

Google Data Cloud provides several curation accelerators designed to reduce the time-to-insight and automate these workflows.

1. Cloud Storage auto-discovery for semi-structured data

The first step in modern curation is eliminating the manual effort of cataloging dark data in Cloud Storage.

Automatic data discovery: The automatic discovery feature in Dataplex Universal Catalog scans GCS buckets to automatically create external tables for structured data and catalog the metadata.
Ad-hoc analysis: This allows for immediate, Gemini-powered analysis via vibe querying to assess value and quality without having to load the data with a traditional ETL process.
Unified governance: This also lets you apply fine-grained access control and automated metadata generation directly on the raw storage layer, ensuring security and governance are baked in right from the start.

2. Metadata curation and augmentation

Curation acceleration relies on moving from columns and rows to a semantic understanding of the data.

Automated insights: Data insights automatically generates column descriptions, relationship graphs, along with suggested questions in natural language. This helps speed up metadata documentation and accelerate initial exploration and analysis when facing new or unfamiliar data.
Grounding Conversational Analytics: These insights later serve to ground conversational analytics in your data, giving agents the additional context to understand how assets relate to your business. This ensures more accurate responses when you chat with your data using natural language.

3. Integrated governance: Quality, profiling, and lineage

Trusted curation requires a robust metadata framework that tracks data health and movement.

Data profiling: Data profiling automatically identifies statistical characteristics (e.g., null counts, distribution) to catch anomalies early.
Quality Controls: Users can define and run data quality checks to ensure that data meets organization's quality standards. Auto data quality lets users automate scans, validate data against rules, and log alerts if the data doesn't meet quality requirements.
Lineage tracking: Table- and column-level lineage, allows engineers to trace how data moves through transformations. This transparency accelerates curation making it easier to debug pipeline errors.

4. Agentic workflows for pipeline development

Google Data Cloud introduces AI agents to handle the heavy lifting of code generation for ingestion and transformation.

Data Engineering Agent: This agent allows you to use Gemini in BigQuery to build and manage pipelines using natural language or by passing a technical design document.
Data Science Agent: Integrated into Colab Enterprise/BigQuery Notebooks, Data Science Agent automates exploratory data analysis (EDA) and generates Python/PySpark code for complex ML-ready pipelines.

5. Catalog-driven asset discovery and data products

To prevent redundant work in large organizations, curation must focus on reuse and internal marketplaces.

Discovery first: Before building new pipelines, teams use the Dataplex Data Catalog to discover existing assets.
Data products: Data is published as data products enriched with logical grouping of data assets, formally packaged to be discoverable, trusted, and accessible for solving specific business problems.
BigQuery sharing (formerly Analytics Hub): This enables in-place sharing, allowing internal and 3rd party teams to access curated data without moving or copying it, which maintains a single source of truth.

6. Built-in AI functions for multi-modal data curation

As enterprises generate increasing amounts of multi-modal data, curation now extends to unstructured formats like images, audio, and documents. The following capabilities address these evolving needs:

SQL reimagined with generative AI functions: By using standard SQL operators, data teams can classify and rank data by quality or criteria without specialized ML expertise. BigQuery AI functions allow users to perform sentiment analysis, summarization, and entity extraction directly within a SQL statement.
Embeddings generation: Curation pipelines can now generate vector embeddings to enable use cases like similarity searches, product recommendations, log analytics, entity resolution and deduplication and more across massive datasets.
Multimodal tables: Multimodal tables let you Integrate unstructured data into standard tables and work with multimodal data with SQL.

7. Real-time curation with continuous queries

For real-time curation, BigQuery provides simplified experience enabling no-code ingestion and SQL based transforms for constant data movement.

Pub/Sub to BigQuery: Direct subscriptions allow for no-code ingestion of streaming data into BigQuery tables.
Continuous queries: Continuous queries are SQL statements that run continuously, processing incoming data in real-time. Curated output can be immediately streamed to Pub/Sub, Bigtable, or Spanner to power downstream applications and real-time dashboards.

In summary, these curation accelerators remove the slow, manual work of cleaning and organizing data by automating the most time-consuming steps. Spend less time prepping and more time making decisions — explore these curation accelerators today to get started.

Data Studio returns as new home for Data Cloud assets

Fri, 10 Apr 2026 16:00:00 +0000

In today's data-rich environment, organizations possess vast amounts of information. Yet, bridging the gap between that data and the users who need to make daily, informed decisions remains a challenge. Users need a single place to curate and analyze their data from the many different sources that impact their business each day.

We are sharing the next step in our mission to solve this challenge and reintroducing a beloved and familiar name, Data Studio (formerly Looker Studio).

In addition to its powerful data visualization capabilities, Data Studio is playing a significant role in the AI era serving Google Data Cloud content. With Data Studio, you have a single place to browse and interact with a variety of Google data sources and assets — from Data Studio reports, to BigQuery conversational agents, to data apps built in Colab notebooks.

Data Studio: reports, data apps, and conversational agents in one place

Extending our vision for analytics in the AI era

Since bringing Data Studio to the Google Cloud family five years ago, customers have continued to innovate with Data Studio as a place to visualize and share their data assets. Meanwhile, as AI becomes a critical component of practically every business, we’ve heard from our customers that they need a single place to save, organize and browse their data assets.

As part of this reintroduction, with Looker as our enterprise business intelligence platform, we are evolving Data Studio to complement the Looker platform, independently. As we have redesigned Data Studio, Looker has also recently seen significant investments in its self-service and visualization offerings, including agentic capabilities for use cases that demand trusted, governed data powered by a central semantic model.

We believe the new Data Studio is the ideal choice for personal data exploration — a place to craft ad-hoc reports, and quickly visualize data across Google’s ecosystem, from BigQuery to Google Sheets and Ads. This strategic differentiation ensures customers have the right tool for the right job.

Two flavors: Data Studio and Data Studio Pro

The new Data Studio experience is available in two editions.

Data Studio continues to offer powerful, no-cost individual analysis and visualization, serving as the on-ramp for creating and sharing ad-hoc reports, transforming data to an interactive dashboard in minutes.
Data Studio Pro is designed for scaling teams and organizations that need more agility and control, including AI features and deep integration with Google Cloud for enterprise-grade security, management, and compliance capabilities. Pro licenses can be purchased directly from the Google Cloud console or the Google Workspace Admin Console.

Upgrading to the new Data Studio should be largely transparent for the many users who count on this product in their daily work. All existing reports, data sources, assets and users will be transitioned to the new experience with no action on your part. Learn more about what’s coming to Data Studio and our vision for Data Cloud and Analytics at Google Cloud Next ‘26 later this month.

Openness without compromises for your Apache Iceberg lakehouse

Wed, 08 Apr 2026 16:00:00 +0000

Today, at the Apache Iceberg Summit in San Francisco, we are announcing the preview of read and write interoperability between BigQuery and Iceberg-compatible engines, including Trino, Spark, and others in Apache Iceberg tables in Google-managed Iceberg REST Catalog. With this new capability, you get the benefits of enterprise-grade native storage for your lakehouse without sacrificing Iceberg's openness and flexibility.

Why it matters: If you're building a lakehouse today, you're probably using Apache Iceberg, which has gained massive popularity among data platform teams that need to support multiple compute engines (like Spark and BigQuery) that access the same data for different workloads. However, we consistently hear from customers that achieving openness often requires compromises.

Compared to using enterprise storage, there’s often price-performance overhead on using Iceberg, wiping out the cost benefits of a single-copy architecture. In order to make Iceberg work for all production use cases, data teams have to invest in custom infrastructure to handle real-time streaming, build complex pipelines to replicate operational data, and navigate fragmented governance across different compute engines. Ultimately, these limitations become bottlenecks to innovation.

Over the years, Google has purpose-built storage infrastructure to solve these exact challenges at scale, powered by highly scalable, real-time metadata, unified governance, and deep vertical integration across Cloud Storage, metadata, and various query engines. We are making this infrastructure available directly in Iceberg.

This enables access to BigQuery's advanced runtime, automatic table management, partitioning, multi-statement transactions, and change data replication for Google-managed Iceberg REST catalog tables. These features will be available in preview for Google-managed Iceberg REST catalog tables and will be generally available (GA) for BigQuery-managed Iceberg tables, coming next month.

Write and read interoperability across engines

Previously, customers building lakehouses chose between Iceberg tables in the Google-managed Iceberg REST catalog or tables managed by BigQuery based on their primary ETL engine. That means that customers relying on Apache Spark for ETL to Iceberg REST Catalog tables couldn’t write through BigQuery or use its storage management features.

With this preview, you can create, update, and query Iceberg tables in the Google serverless Iceberg REST catalog with BigQuery or other Iceberg-compatible engines such as Spark, Flink, Trino and others. This two-way read and write interoperability enables data teams to implement multi-engine use cases on a single table type in a fully open manner, using native Iceberg libraries.

Additionally, Iceberg REST Catalog offers table-level access controls using credential vending for uniform governance across BigQuery, Spark and other compute engines that query or modify your Iceberg tables.

Google Cloud also supports a robust ecosystem of partners integrated with the Iceberg REST Catalog across data platforms and engines, transformation and ingestion services, and governance platforms. We work closely with the Iceberg ecosystem to strengthen these partnerships with many more to come.

Improved price-performance with BigQuery and Spark

Automate table management

Achieving strong query performance on Apache Iceberg tables out of the box can be hard. You need to choose the optimal target file size (which tends to be different for different compute engines), data organization strategy (partitioning and sort-order choices have their tradeoffs), and take care of table management to avoid small files problems and metadata bloat.

Apache Iceberg lakehouse customers can now offload table maintenance — compaction and garbage collection — to Google Cloud BigLake, which optimizes performance for you. In addition to Iceberg tables in BigQuery, it will be available for Google-managed Iceberg REST catalog tables in preview, coming next month. You can opt-in to table management by setting a single property, and improve your BigQuery performance on the industry standard TPC-DS 10T benchmark by ~40%.

Improve BigQuery price-performance with advanced runtime

BigQuery advanced runtime offers a set of performance enhancements designed to automatically accelerate analytical workloads without requiring user action or code changes. In particular, it extends the vectorized query execution enhancements in BigQuery to open table formats. Advanced runtime will be available in preview for Google-managed Iceberg REST catalog tables and in GA for BigQuery-managed Iceberg tables, coming next month. According to an internal TPC-DS 10T benchmark, advanced runtime can help additionally accelerate BigQuery query performance on Iceberg tables, providing 2x faster performance vs. a self-managed approach based on internal benchmarking.

Chart based on benchmarks from internal data and testing.

Accelerate Spark performance with Lightning Engine

Apache Spark is a leading compute engine for Apache Iceberg lakehouses, for use cases ranging from ETL to feature engineering. However, achieving high performance and cost efficiency for Spark workloads at scale can be challenging. Lightning Engine accelerates Apache Spark query performance by over 4 times compared to open source Spark (based on a TPCH-like benchmark).

Optimize table layout with BigQuery partitioning and clustering

Many open-source libraries and engines rely on Iceberg table partitioning for effective data pruning. BigQuery time-based partitioning will be available in preview for Google-managed Iceberg REST catalog tables and will be generally available (GA) for BigQuery-managed Iceberg tables, coming next month. Additionally, when you are creating Iceberg tables in BigQuery, you can define clustering columns to organize data in Parquet files, helping to achieve optimal query performance and avoiding common issues with partitioning such as high-cardinality columns, small partition inefficiencies, and multiple filter columns. For example, one common pattern is to combine time-based table partitioning with clustering on other dimensions that are frequently used for query filtering, such as region, store, etc.

Advanced analytics with Apache Iceberg

Streaming with Apache Iceberg

To operationalize real-time analytics with Iceberg, you can leverage BigQuery’s Vortex streaming infrastructure for high-throughput ingestion with zero-read latency. This removes the need for bespoke infrastructure, addresses small file issues, and lets you query data immediately from the streaming buffer to achieve near-zero read latency. This feature is generally available for BigQuery-managed Iceberg tables and will be available in preview for Google-managed Iceberg REST catalog tables, coming next month.

Replicate data from operational databases to Iceberg tables with Datastream

You can now easily replicate data from a variety of operational datastores, including MySQL, Postgres, SQLserver, Oracle, Salesforce, and MongoDB , into managed Iceberg tables in BigQuery using Datastream integration (GA).

Illustration of Datastream creation to replicate MySQL data to managed Iceberg tables in BigQuery.

Incremental processing with change data capture ingestion to Iceberg tables

The BigQuery storage write API’s change data replication feature lets you stream insert, update, and delete changes from OLTP databases to Iceberg tables in real time, removing the need for complex MERGE-based ETL pipelines. This feature will be available in preview for Google-managed Iceberg REST catalog tables and generally available (GA) for BigQuery-managed Iceberg tables, coming next month.

Illustration of change data capture ingestion to a managed Iceberg table in BigQuery.

Multi-statement transactions

Many analytics workloads require transactions that span multiple tables to commit or roll back changes atomically. This provides consistency across logical groups of tables, synchronizes dimensions and fact tables, and simplifies multi-stage ETLs. You can now leverage BigQuery multi-statement transactions to radically simplify complex multi-table processing with Iceberg. This feature will be available in preview for Google-managed Iceberg REST catalog tables and generally available (GA) for BigQuery-managed Iceberg tables, coming next month.

Illustration of a multi-statement transaction in a managed Iceberg table in BigQuery.

Get started

With bidirectional interoperability across BigQuery and other Iceberg-compatible engines on Google-managed Iceberg REST catalog tables, you can modernize your lakehouse with Apache Iceberg without compromising on performance, governance, or advanced analytics.

Ready to start building today? Learn more about our lakehouse capabilities and explore our quickstart guides.

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