Learn Apache Flink and Beam for real-time data streams

🚀 Introducing Scalability for GeoPandas in Apache Sedona The new GeoPandas API for Apache Sedona allows GeoPandas developers to seamlessly scale their geospatial analysis beyond the limits of a single machine. By combining the intuitive, Pythonic GeoPandas API with Apache Sedona’s distributed processing capabilities, you get the best of both worlds: familiar syntax with planetary-scale performance. With this new API, you can: ✅ Keep the familiar GeoPandas syntax while running computations in a distributed, scalable environment. ✅ Simplify your workflow that removes the need to choose or move data between GeoPandas and Apache Sedona. ✅ Scale geospatial processing seamlessly as your datasets and analytical workloads grow. ➡️ Read the full blog and follow along with the example: https://bit.ly/4o4y64j

Sail outperforms Spark by nearly 4x in total query time on the derived TPC-H benchmark at 100GB (64 partitions)—completing all 22 queries in just ~103 seconds, compared to ~387 seconds on Spark. This performance leap is achieved through: • Rust-native execution that eliminates JVM overhead and garbage collection entirely. • Columnar in-memory processing via Apache Arrow for efficient data access. • Optimized DAG planning and lightweight task scheduling using DataFusion’s physical execution engine. • Significantly lower memory usage per task, enabling faster execution with fewer resources. With full Spark Connect compatibility and no code rewrite required, the switch to ~4x faster workloads for 6% the cost couldn’t be easier. Give Sail a try! https://lnkd.in/gFG_GbMx Learn more about our benchmark results: https://lnkd.in/gZvBfp49

Over the past few days, I focused on enhancing my understanding of streaming with Spark by building a small end-to-end pipeline using Kafka, Spark Structured Streaming, and BigQuery. The flow includes: - A Python producer generating simulated taxi ride events - Events being published to Kafka topics - Spark Structured Streaming consuming and processing the events in real time - Data being written into BigQuery for further analysis I have documented the setup steps, architecture, and repository structure in the README file. Do check it out. GitHub repo: https://lnkd.in/gQd5rsMk

The CAP Theorem: what it means in real systems? The CAP theorem is one of those concepts that often comes up in interviews, yet is frequently misunderstood. Let’s break it down clearly. ⸻ What CAP says In a distributed system, when a network partition happens (and it eventually will), you must choose between: • Consistency (C): Every read receives the most recent write • Availability (A): Every request receives some response (even if stale) • Partition Tolerance (P): The system continues working despite network failures You can’t have all three at the same time. Partition tolerance is non-negotiable in distributed systems, so the real trade-off is Consistency vs Availability. ⸻ Practical examples • CP systems (Consistency + Partition tolerance) • Zookeeper, etcd, HBase • Sacrifice availability to keep data strongly consistent • AP systems (Availability + Partition tolerance) • Cassandra, DynamoDB, Couchbase • Sacrifice strict consistency to stay highly available (eventual consistency) ⸻ Common misconceptions • CAP isn’t about everyday performance — it’s about behavior during partitions • It doesn’t mean you only ever get 2 of 3; it means under failure you can’t guarantee all three • Real systems often mix strategies (some operations favor C, others A) 👉In your current systems, do you lean more towards CP or AP, and why? #captheorem #distributedSystems #architecture #backend #scalability #databases

Learn how to quickly install Kibana using Docker. Follow our step-by-step guide to set up and run Kibana in a Docker container.

In Apache Spark, everything revolves around two types of operations: - Transformations are like preparing your recipe (map, filter, repartition). - Actions are like finally serving the dish (collect, count, reduce). Here’s the text from the image you uploaded: "Transformations are Lazy, Execution is not done immediately, it becomes a part of execution plan & when action is called then entire execution plan gets executed"

New Lonboard release and new demo! Integrating marimo and Apache DataFusion to visualize the NYC taxi dataset. https://lnkd.in/egQh7pa7 I've been working on geospatial extensions for the Apache DataFusion SQL query engine, using GeoArrow as the underlying compute layout. It's early, but I'm working on fleshing out the PostGIS API. And there are Python bindings too! https://lnkd.in/eX8eApKZ. This was my first time using marimo and it was a joy to use! And its interactivity plays really nicely with Lonboard. Lonboard's 0.12 release improved the support for GeoArrow data types, and is moving towards being fully GeoArrow-native. Shapely is no longer a required dependency! https://lnkd.in/ergExzmu

Live from #FlinkForward, Yuan Mei shares VERA-X, the first native vectorized engine for Apache Flink. What does this mean? Blazing speed for data processing that supports “I need it RIGHT NOW” use cases (like fraud detection). Her recording will be available soon, but in the meantime, you can learn more in the blog from Ververica | Original creators of Apache Flink® https://lnkd.in/g_wFM64B

InfluxDB 3 Enterprise makes time series management simple, scalable, and fast to deploy. With cloud-native, diskless architecture, you get instant failover, seamless scaling, and unlimited data retention. Deploy single node, multi-node, or hub configurations and run custom Python logic to automate insights. Build the system that adapts to your workload, not the other way around: https://bit.ly/4lC91wQ

Recently, I had a wonderful opportunity to contribute to the Apache Polaris data catalog for Apache Iceberg 🌟. The project is currently under active development, and sometimes it’s necessary to quickly deploy a test environment to connect and experiment with its functionality. In this short article, I share an example of how I managed to automate some of the routine tasks related to deployment in K8s using Skaffold. I’ve also included examples of how to handle this using native kubectl tools. Enjoy! 🕹️