Igor De SouzaKafka vs Flink vs Spark Streaming: What Nobody Tells You Before You Pick One
InfoQ#Pinterest launched a next-gen CDC-based ingestion framework.
Using #ApacheKafka, #ApacheFlink, #ApacheSpark & #ApacheIceberg, they achieved:
• Latency cut from 24+ hours to 15 minutes
• Processing of only changed records
• Support for incremental updates & deletions
• Petabyte-scale data across 1,000+ pipelines
Win: optimized cost & efficiency!
Read the architectural deep dive on InfoQ 👉 https://bit.ly/4rMJB2H
Estimator - Smart capacity planning for Apache Flink workloads
Why Apache Flink Is Not Going Anywhere
https://www.streamingdata.tech/p/why-apache-flink-is-not-going-anywhere
Frans KingFlink Stateful Functions https://nightlies.apache.org/flink/flink-statefun-docs-stable/ hasn't had an update in several years and is still compiled against an old version of Apache Flink.
I spent some time during the holidays uplifting it to Flink 2.2.0 https://github.com/fransking/flink-statefun/commit/25b2dfcc3feafd701eb2fec32d413fab56e1cfe6
Notionally it appears to be working as expected.
Stop 3 AM Pager Duty: Enforce Data Contracts with Apache Kafka & Apache Flink
Apache Flink 2.2.0: Advancing Real-Time Data + AI and Empowering Stream Processing for the AI Era
Apache Flink 2.2.0: Advancing Real-Time Data + AI and Empowering Stream Processing for the AI Era
The Apache Flink PMC is proud to announce the release of Apache Flink 2.2.0. Flink 2.2.0 further enriches AI capabilities, enhances materialized tables and the Connector framework, and improves batch processing and PyFlink support. This release brings together 73 global contributors, implements 9 FLIPs (Flink Improvement Proposals), and resolves over 220 issues. Apache Flink 2.2 ushers in the AI era by seamlessly integrating real-time data processing with artificial intelligence. It introduces ML_PREDICT for large language model inference and VECTOR_SEARCH for real-time vector similarity search, empowering streaming AI applications.
My Apache Flink Pains ...
Flink is powerful, but it's a brutal babysitting job. From "GC death spirals" to week-long schema evolution nightmares, the operational burden is immense.
Kafka Streams vs Apache Flink: A Pragmatic Comparison for Stream Processing (And why you should start with Kafka Streams !)