Apache SeaTunnel: A Next‑Generation Data Integration Platform for ETL/ELT and OLAP

Apache SeaTunnel is presented as a new‑generation data integration platform for the EtLT era, positioned between traditional ETL and ELT, aiming to provide lightweight extraction, transformation, and loading across heterogeneous data sources and targets.

The platform’s core design includes six parts: project introduction, core functionalities, OLAP scenario applications, community roadmap, WhaleTunnel product features, and a Q&A session.

SeaTunnel’s architecture decouples connectors from execution engines, offering Source, Transform, Sink, Checkpoint, and Engine APIs that support multiple engines (Flink, Spark, Zeta) and enable both batch and streaming modes with a single configuration.

Key connector capabilities include simple configuration, extensive source ecosystem, monitorable synchronization stages, full‑scene support (micro‑batch, offline, real‑time, CDC), data consistency guarantees, and low resource consumption through optimized parallelism and connection pooling.

The checkpoint design orchestrates Split enumeration, Source reading, Sink writing, snapshot creation, and final aggregation, with engine‑specific implementations for Flink, Spark, and Zeta.

Model inference automatically derives target table schemas from source catalogs, handling type conversion, length, precision, and character encoding to ensure accurate table creation and data mapping.

Multi‑table synchronization allows specifying multiple source tables, optional field selection, global where clauses for incremental sync, and dynamic target table naming expressions.

SeaTunnel Zeta, a new native synchronization engine, provides master/worker separation, distributed memory grid for state storage, WAL‑based fault tolerance, and fine‑grained monitoring, achieving 30‑50% higher throughput than DataX and outperforming major SaaS tools.

In OLAP scenarios, SeaTunnel supports batch and real‑time data sync to engines such as Doris, StarRocks, ClickHouse, and Greenplum, offering automatic model inference, auto‑DDL generation, multi‑table writes, exactly‑once semantics, and high‑throughput loaders.

The community roadmap outlines upcoming features like a new Zeta master/worker architecture, SQL‑based job creation, ClassLoader isolation for heterogeneous plugins, and CDC resource release improvements.

WhaleTunnel, the commercial version, adds a visual interface, deep integration with DolphinScheduler, richer data source support (including Chinese‑origin databases), visual model inference, enhanced monitoring, and DDL change handling.

The article concludes with a Q&A covering connection to Xinchuang databases, CDC full‑load strategies, performance metrics, and real‑world adoption by companies such as JD.com and Bilibili.