#Apache Flink

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#apache-flink #apache-kafka #schema-evolution #event-driven-architecture #schema-consolidation #streaming-sql
Scala
fromInfoQ
4 days ago

The Schema Proliferation Problem in Kafka and Flink Pipelines: How to Solve It

One-to-one event-to-schema mapping scales poorly, causing fragmented queries, maintenance overhead, and schema drift.
Event schemas with 80–95% structural overlap can be consolidated using discriminator enum fields into fewer tables and simpler consumer queries.
Nullable attribute blocks support backward-compatible schema evolution when adding new event variants.
A layered adapter design separates transformation logic from framework integration, easing consolidation implementation and testing in Apache Flink pipelines.
Schema design aligned to consumer access patterns simplifies queries and reduces long-term maintenance overhead.
Software development
fromInfoWorld
3 months ago

Why your next microservices should be streaming SQL-driven

Streaming SQL with UDFs, materialized results, and ML/AI integrations enables continuous, stateful processing of event streams for microservices.
Artificial intelligence
fromTechzine Global
9 months ago

Confluent introduces Streaming Agents for real-time AI agents

Confluent launched Streaming Agents to enable enterprise-grade, real-time AI agents that use up-to-date streaming data and integrate with tools, databases, and models.
fromInfoWorld
9 months ago

Apache Flink integrates AI for real-time decision-making

With the 2.1 release, Apache Flink also now supports Process Table Functions (PTFs), the most powerful kind of function for Flink SQL and Table API.
Data science
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