Apache Flink 2.2.0 enhances real-time data processing by integrating AI capabilities, introducing new functions like ML_PREDICT for large language models and VECTOR_SEARCH for vector similarity searches. The release also improves materialized tables, batch processing, and connector frameworks, addressing over 220 issues.

The article explores the ingestion of Debezium change events from Kafka into Apache Flink using Flink SQL. It details the use of two main connectors—the Apache Kafka SQL Connector and the Upsert Kafka SQL Connector—highlighting their functionalities in both append-only and changelog modes, along with key configurations and considerations for processing Debezium data effectively.

Flink SQL treats all objects as tables, addressing the complexities of dynamic and static tables in both streaming and batch contexts. The article explores how changelogs work in Flink SQL, particularly focusing on LEFT OUTER JOIN operations, and highlights the implications for state management and data updates within a streaming environment.