Incremental Computation in Big Data: Flink Materialized Table and Paimon

The article explains how Flink 1.20’s Materialized Table combined with Paimon’s changelog storage enables incremental computation that unifies batch and streaming workloads, delivering minute‑level latency at lower cost, illustrated by a materialized‑table example while noting current streaming‑only support and future batch extensions.

Big DataFlinkIncremental Computation

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Incremental Computation in Big Data: Flink Materialized Table and Paimon

Big Data Technology & Architecture

Aug 5, 2024 · Big Data

Key Features of Apache Flink 1.20: Materialized Tables, DISTRIBUTED BY, and State/Checkpoint Optimizations

The article reviews Apache Flink 1.20, highlighting the new Materialized Table concept, the DISTRIBUTED BY support for load‑balanced storage and join performance, and state/checkpoint file merging improvements, while providing code examples and practical insights for users.

Apache FlinkBig DataCheckpoint Optimization

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Key Features of Apache Flink 1.20: Materialized Tables, DISTRIBUTED BY, and State/Checkpoint Optimizations