Building a Real-Time Data Warehouse with Flink: Hive Integration, Upsert‑Kafka, and CDC Connectors
This tutorial explains how to use Apache Flink 1.12 to construct a unified streaming‑batch data warehouse by integrating Hive via HiveCatalog and HiveDialect, performing read/write operations, configuring upsert‑Kafka sinks, and leveraging Flink CDC connectors for change data capture from MySQL and other sources.
Flink 1.12 provides built‑in support for Hive integration, allowing users to persist metadata in Hive Metastore via HiveCatalog, read and write Hive tables in both batch and streaming modes, and switch between default and Hive SQL dialects for DDL/DML.
The article details steps to add required Hive and Hadoop dependencies, configure sql-client-defaults.yaml, create Hive‑compatible and generic tables, and perform temporal joins with the latest Hive partitions using streaming source options.
It also introduces the upsert‑Kafka connector, describing its requirement for primary keys, key/value serialization formats, and configuration parameters such as value.fields-include and key.fields-prefix, with example DDL and insert statements.
Furthermore, the guide covers Flink CDC connectors, including MySQL‑CDC and Canal‑JSON, showing how to create CDC source tables, capture change events, and write aggregated results to Kafka using the changelog-json format.
Throughout, practical SQL examples, table properties, and execution hints are provided to help readers build a real‑time data warehouse that combines batch and streaming processing.
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