Building Stream‑Batch Integrated ETL with Flink SQL: Data Warehouse and Data Integration
This article explains how Flink SQL can be used to construct a unified stream‑batch ETL pipeline for data warehouses and data lakes, covering data integration, CDC support, streaming writes to Hive and Iceberg, and various join techniques such as regular, interval, and temporal joins.
Flink SQL enables a seamless stream‑batch integrated ETL process that unifies data extraction, transformation, and loading for both real‑time and batch data warehouses. The article begins with a definition of data warehouses, emphasizing integration as the core challenge, and describes traditional architectures that separate real‑time and offline pipelines, leading to duplicated work and inconsistent data models.
Flink addresses these issues by providing native CDC support ( format = 'debezium-json'), powerful temporal joins, and the ability to write directly to Hive with automatic small‑file compaction. The MySQL CDC connector ( connector = 'mysql-cdc') simplifies database synchronization, while the upsert‑kafka connector ( connector = 'upsert-kafka') offers an efficient CDC storage format.
Key benefits of the stream‑batch architecture include unified base data, consistent stream‑batch results, faster offline warehouse freshness, and reduced component maintenance costs. The article then details each ETL stage:
Data Ingestion : Logs can be collected via Flume, Filebeat, Logstash, etc., while database changes are captured using CDC tools (Canal, Debezium, Maxwell) or Flink’s native CDC connector.
Data Loading : Flink SQL can stream data into Hive with automatic compaction and into Iceberg catalogs, supporting both append and CDC streams.
Data Enrichment (Transformation) : Various join types are discussed—Regular Join for generic dual‑stream joins, Interval Join for time‑bounded joins, Temporal Join for dimension table lookups (both lookup‑DB and changelog‑based), and Temporal Join with Hive partitions.
Temporal joins provide high real‑time performance by materializing dimension tables in Flink state, eliminating external I/O, and allowing precise versioned joins using event‑time watermarks. The article concludes with a capability matrix showing Flink’s extensive integration with relational databases, key‑value stores, message queues, data lakes, and data warehouses, and outlines future enhancements.
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