Apache SeaTunnel — High-Performance Data Integration Engine

What it is

Apache SeaTunnel is a distributed data integration engine that synchronizes data between 100+ sources and sinks in both batch and streaming modes. It supports databases (MySQL, PostgreSQL, Oracle), data warehouses (BigQuery, Snowflake, Redshift), file systems (HDFS, S3, local), and message queues (Kafka, Pulsar). Jobs are defined in YAML or JSON configuration files.

SeaTunnel targets data engineers who need to move data between heterogeneous systems at scale. It suits ETL pipelines, data lake ingestion, database migration, and real-time data synchronization scenarios.

How it saves time or tokens

This workflow provides the download, installation, and a sample job configuration. Instead of writing custom data pipeline code for each source-sink pair, you define a YAML config and SeaTunnel handles connection management, parallelism, fault tolerance, and data type mapping.

How to use

1. Download and install SeaTunnel:

wget https://dlcdn.apache.org/seatunnel/2.3.5/apache-seatunnel-2.3.5-bin.tar.gz
tar -xzf apache-seatunnel-2.3.5-bin.tar.gz
cd apache-seatunnel-2.3.5

1. Create a job configuration:

# config/mysql_to_postgres.conf
env {
  parallelism = 4
  job.mode = "BATCH"
}

source {
  Jdbc {
    url = "jdbc:mysql://localhost:3306/source_db"
    driver = "com.mysql.cj.jdbc.Driver"
    user = "root"
    password = "password"
    query = "SELECT * FROM orders"
  }
}

sink {
  Jdbc {
    url = "jdbc:postgresql://localhost:5432/target_db"
    driver = "org.postgresql.Driver"
    user = "postgres"
    password = "password"
    table = "orders"
  }
}

1. Run the job:

./bin/seatunnel.sh --config config/mysql_to_postgres.conf

Example

# Streaming from Kafka to Elasticsearch
env {
  parallelism = 2
  job.mode = "STREAMING"
  checkpoint.interval = 10000
}

source {
  Kafka {
    bootstrap.servers = "kafka:9092"
    topic = "events"
    format = "json"
  }
}

sink {
  Elasticsearch {
    hosts = ["http://elasticsearch:9200"]
    index = "events-${now}"
  }
}

Related on TokRepo

• Database tools -- Data processing and integration solutions
• Automation tools -- Workflow automation for data pipelines

Common pitfalls

• JDBC connector requires the database driver JAR in the lib directory. SeaTunnel does not bundle proprietary drivers like MySQL or Oracle.
• Parallelism settings higher than source partitions waste resources. Match parallelism to the data distribution of your source.
• Streaming mode requires checkpoint configuration for fault tolerance. Without checkpoints, a failure restarts the job from the beginning.