Building Real-Time Data Synchronization Pipelines with Apache SeaTunnel

In the era of rapid digital transformation, data has become a core driver for enterprise decision‑making and innovation, making real‑time data synchronization a critical component for ensuring consistency, timeliness, and completeness across systems.

Apache SeaTunnel is an open‑source, distributed data integration platform focused on big‑data scenarios. It supports both stream and batch processing, offering a unified solution that can handle billions of records daily and is already deployed in production environments of major enterprises.

The presentation is organized into four parts: background introduction, hands‑on experience, challenges and solution approaches, and future outlook.

SeaTunnel’s architecture centers on a unified API ecosystem, including Source, Sink, Table, and Engine APIs, allowing plugins to be written once and run on multiple execution engines such as its native Zeta engine, Flink, and Spark. This design enables flexible data source integration (databases, message queues, files, cloud storage, SaaS APIs) and high‑performance loading.

Key features include:

Connector plugin architecture with source, sink, and transform APIs, supporting parallelism, state checkpointing, and two‑phase commit for exactly‑once semantics.

CDC capabilities that capture change data from databases (MySQL, PostgreSQL, Oracle, etc.) with snapshot and binlog phases, ensuring data consistency through water‑mark tracking and merge processes.

Lightweight row‑level and table‑level transform plugins for data cleaning, schema evolution, and DDL synchronization.

State management via distributed snapshots, enabling fault‑tolerant recovery and consistent checkpoint handling across tasks.

Classloader isolation for source and sink plugins to avoid dependency conflicts at both task and job levels.

Type mapping and automatic table creation APIs that abstract source data types to target storage types, supporting various DDL strategies.

The hands‑on demo covers installation (binary download or source compilation), task configuration (env, source, transform, sink sections), and job submission using the seatunnel.sh client in local or remote modes.

Challenges discussed include complex data source diversity, resource optimization during historical data loading versus incremental CDC, dynamic table addition/removal, and multi‑sink synchronization using shared in‑memory queues.

Future directions for SeaTunnel involve expanding engine support for DDL synchronization (e.g., Flink), adding more sink connectors (targeting around twenty new components), enhancing MQ‑based CDC handling, extending DDL‑aware transform plugins, and improving support for sharding and partitioning scenarios.

Overall, SeaTunnel provides a comprehensive, extensible framework for building scalable, real‑time data integration pipelines in big‑data environments.