Snir Zur·2d agoThe Partition Key We Thought We Couldn’t ChangeWas partitioning by customer really the only option?
Surya Teja·6d agoKafka Moves Data. Flink Thinks About It. (Part 2)Hello Pals,I owe you one. At the end of Exploring Apache Flink I promised a hands-on Flink + Kafka example, and here it finally is (better…
ShreeVarada·6d agoEvent Time vs.A practical, technical deep dive into how Spark processes time-dependent streaming data, handles late events, and decides when results are…
InIntellytics SolutionsbyBrijesh Dave·6d agoTracking Ships in Real Time with Kafka, Databricks, and Delta LakeReal-time streaming pipelines can feel like trying to repair a high-speed locomotive while barreling down the tracks. When you build…
Kirill Fedorov·Oct 28Kafka Retention Does Not Start When Your Consumer FailsHow byte limits, partition skew, compaction, and reset policies change a pipeline’s recovery window
InVenturebyM. Ali Khan·Oct 26What Happens When an API Takes Too Long to Respond? Enter Web StreamsWhen an API performs a long-running operation, such as generating an AI image, processing a video, exporting a large report, or executing a…
Katya Gorshkova·Oct 23Hands-on with Flink — Part 10: Joining Streams with Reference DataPrevious parts:
watanaberyunosuke·Oct 15Messaging for utility meteringMessaging architecture series, part 2 of 5. Sources are linked inline and listed at the end. Written September 2026; product details change…
InData Science CollectivebyVishnu Vardhan Kothapalli·Oct 12From Kafka to Spark: What Really Happens Inside a Streaming Pipeline?An event enters Kafka. Spark picks it up. But what happens behind the scenes when data flows, failures strike, and processing resumes?A response icon1A response icon1
Alisson T. Bucchi·Oct 8Your Tests Passed. Feeling Lucky? Let’s Flirt With the System...There is a beautiful moment in every software project.
