seatunnel

Why we included this project

Teams moving serious volumes of data between systems will find SeaTunnel useful long after its Apache ETL roots stop being the point. It is a distributed integration layer that reads from databases, message queues, object stores, and files, applies light transforms in between, and lands the results in warehouses, lakehouses, search systems, and vector stores, all defined in one config file that runs unchanged on its own Zeta engine, Flink, or Spark. For AI teams, the interesting part is where it is headed: embedding transforms, vector-store sinks like Milvus and Qdrant, and work toward keeping vector indexes consistent as source documents change. Anyone building RAG or multimodal pipelines who does not want to hand-roll document-to-vector movement gets the same machinery for ordinary batch, streaming, and CDC jobs too. It is a data plumbing tool that now also feeds AI applications, not a model or agent framework.