Rippling transforms data architecture with AWS for innovation

Working alongside Amazon Web Services (AWS), we've built new transformative products—Custom Objects and App Studio—on a unified data foundation. Our new products have made it possible for our enterprise customers to build hundreds of new business applications, ranging from vehicle fleet management to product launch trackers, adding their own bespoke datasets and business logic alongside the company’s built-in suite of services. “Using AWS databases, we help our customers automate workflows and integrations, get the answers they want, and accelerate decision-making,” says Gerhard Esterhuizen, SVP of Platform at Rippling. “We’re ultimately building software to support almost all business applications,” says Albert Strasheim, CTO at Rippling. Shoutout to Sahin Olut and the rest of our incredible engineering team who worked on the project 💪 ➡️ Learn more about App Studio here: https://lnkd.in/e952Ej7m

🚨 Still stitching together disconnected tools across HR, IT, and Finance? We’ve partnered with AWS to launch Custom Objects and App Studio at Rippling — giving teams the power to build custom apps without code on a unified platform. ✅ Automate workflows ✅ Connect siloed data ✅ Build apps for planning, approvals, ops & more — all tailored to your org Hundreds of companies are already moving faster and working smarter. 📊 This isn’t just a new tool. It’s a new standard. #Rippling #HRTech #FinanceOps #ITAutomation #NoCode #FutureOfWork

Querying Smarter: How AWS Athena Unlocks S3 Data Without a Warehouse For years, querying large-scale data meant spinning up expensive warehouses or clusters, ingesting data into rigid schemas, and waiting for results. AWS Athena flips this model on its head by allowing you to run SQL queries directly on data stored in Amazon S3—no infrastructure management, no traditional warehouse overhead. 1. Serverless by Design Athena is fully serverless. There’s no provisioning, patching, or scaling of servers. You point it to your S3 bucket, define the schema, and start querying instantly. You pay only for the queries you run, based on the amount of data scanned. 2. Works Natively with Open Formats Athena supports open file formats such as CSV, JSON, ORC, Avro, and Parquet. This flexibility allows teams to store raw or semi-structured data in S3 and still query it efficiently without additional transformation steps. 3. Integration with Glue Data Catalog Defining and managing schemas at scale can be painful. By integrating with AWS Glue Data Catalog, Athena automatically discovers, catalogs, and manages metadata, making it easier to query data consistently across teams and workloads. 4. Cost-Optimization with Compression and Partitioning While Athena charges per data scanned, techniques like storing data in Parquet format, compressing files, and partitioning datasets drastically reduce query costs and improve performance. Efficient data structuring turns Athena into a cost-effective analytics powerhouse. 5. Eliminating Heavy ETL Workflows With Athena, raw S3 data can be queried directly—reducing the need for complex ETL pipelines. This empowers data teams to focus on insights rather than infrastructure, and makes analytics accessible to a wider range of users. 6. Seamless BI Integrations Athena integrates natively with AWS QuickSight and third-party BI tools. Analysts can connect, visualize, and derive insights in near real-time, without ever moving the data. Athena is not meant to replace every data warehouse use case, but for flexible, on-demand analytics over data lakes, it shines. AWS Athena democratizes analytics by removing the warehouse barrier—making S3 a powerful foundation for fast, scalable, and cost-efficient insights. #AWS #Athena #S3 #CloudComputing #DataAnalytics #Serverless #BigData #ETL #CloudInnovation #TechStrategy

🚀Day-26 of Covering Simplifying Data Management with AWS Glue Data Catalog & Crawlers In today’s data-driven world, one of the biggest challenges is not just storing data but making it discoverable, organized, and ready for analysis. That’s where AWS Glue Data Catalog and Crawlers come into play. 🔹 AWS Glue Data Catalog Think of it as a central metadata repository for all your datasets across S3, RDS, Redshift, and other sources. It automatically keeps track of schema, partitions, and table definitions, making it easier for services like Athena, EMR, and Redshift Spectrum to query data seamlessly. 🔹 AWS Glue Crawlers Crawlers scan your data sources, infer the schema, and automatically populate the Data Catalog. No more manual schema creation! This automation ensures that your data is always up-to-date, structured, and ready for analytics or ETL pipelines. 💡 Why it matters: ✔️ Reduces manual effort in managing metadata ✔️ Ensures consistency across multiple services ✔️ Speeds up the time-to-insight for analysts & engineers. ->Glue Data Catalog + Crawlers = Smarter data discovery + Faster analytics. #AWS #Glue #ETL #DataCatalog #BigData #Analytics 

🚀 Harnessing the Power of AWS Glue for Scalable Data Integration! 🚀 In today’s data-driven world, efficient ETL/ELT pipelines are the backbone of analytics and insights. I’ve been leveraging AWS Glue to simplify and automate data preparation, integration, and transformation at scale. Key highlights of AWS Glue: Serverless ETL: No infrastructure management; focus purely on transforming and moving data. Flexible Data Integration: Seamlessly connects with S3, Redshift, RDS, and more. Automated Schema Discovery: Catalogs your data and keeps metadata updated. Scalable & Cost-Efficient: Handles massive datasets with pay-as-you-go pricing. Using AWS Glue, I’ve been able to: ✅ Streamline complex ETL pipelines. ✅ Improve data quality and governance. ✅ Accelerate analytics readiness for business teams. 💡 Whether you’re building data lakes, operational dashboards, or ML pipelines, AWS Glue is a game-changer for modern data engineering. #AWS #DataEngineering #ETL #CloudComputing #BigData #DataIntegration #Analytics #AWSGlue

Thrilled to share that my work on AWS Glue Zero-ETL Data Ingestion has been published in the AWS Big Data Blog! 🚀 This enhancement makes Salesforce data Ingestion faster, simpler, and more scalable for analytics and ML use cases. 👉 Read the full post here: https://lnkd.in/epsDVRxB #AWS #Glue #ZeroETL #Salesforce #BigData #Analytics #ML

The telecom industry is overflowing with data, but how do we turn it into actionable insights? My latest article dives deep into a modern data analytics architecture built on AWS. Learn how the powerful combination of Amazon S3, AWS Athena, and AWS Lake Formation can help telcos build a scalable, cost-effective data lake.  Ready to unlock the full potential of your large volume of data? Read the full article here:  https://lnkd.in/gngeqCK5

“Confused about ETL vs ELT?” You are not alone. Many people struggle to understand the difference. The key is where the transformation happens and how it fits your data strategy. ETL (Extract, Transform, Load) - Data is transformed before loading into the warehouse - Best when transformation is heavy and the warehouse is not built for raw data - Traditional choice for on-premise systems ELT (Extract, Load, Transform) - Data is loaded first, then transformed inside the warehouse - Leverages modern cloud warehouses with high compute power - Faster, scalable, and better for large, diverse datasets Quick guide: Use ETL when your data needs strict cleaning before storage. Use ELT when you want speed, scalability, and flexibility with cloud data platforms. The right approach depends on your tools, team, and goals. The right decision will take you to the expected outcomes. How is your team handling this? ETL, ELT, or a mix of both? #ETL #ELT #DataEngineering #DataPipeline #DataIntegration #CloudData #BigData

Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control (TBAC) for federated catalogs, extending fine-grained, scalable, and automated data access management across S3 Tables, Redshift, and many other data sources. This means less manual security overhead, seamless collaboration across teams/data domains/business units, and stronger governance that is perfect for modern data mesh strategies and regulatory compliance. Unlock unified and secure data access at scale with TBAC, empowering your analysts, engineers, and data stewards to drive faster insights. SageMaker Unified Studio is taking huge leaps, FAST! https://lnkd.in/gYjxwzSt #AWS #DataGovernance #SageMaker #Lakehouse #DataMesh #Analytics #CloudDataPlatform #SageMakerUnifiedStudio #SageMakerCatalog

Unlocking the Power of Serverless Analytics with AWS Athena In the world of data, speed and simplicity matter. That’s where Amazon Athena shines. 🌐✨ ✅ What is Athena? Athena is a serverless, interactive query service that lets you analyze data directly in Amazon S3 using standard SQL. No infrastructure to manage, no complex ETL needed — just point, query, and analyze. ✅ Why Athena? 🔹 Serverless – Pay only for the queries you run. 🔹 SQL Support – Query your data with familiar SQL syntax. 🔹 Highly Scalable – Works seamlessly with massive datasets. 🔹 Integration – Connects with AWS Glue Data Catalog for schema & metadata management. 🔹 Multi-format Support – Handles CSV, JSON, ORC, Avro, Parquet, and more. ✅ Use Cases 🔹 Quick log analysis (CloudTrail, VPC Flow Logs, ELB logs) 🔹 Building dashboards with BI tools like QuickSight 🔹 Ad-hoc querying for Data Lake exploration 🔹 Cost-effective analytics for unstructured data 💡 Pro Tip: Combine Athena + AWS Glue Data Catalog + S3 Data Lake → you get a powerful, low-cost, serverless data analytics solution. 📊 With Athena, organizations can turn raw data into insights in minutes, fueling smarter decision-making without worrying about infrastructure. #AWS #Athena #DataAnalytics #Serverless #BigData #CloudComputing #DataEngineering