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Paimon

A table format for realtime Lakehouse architectures, enabling unified streaming and batch storage and query with Flink and Spark.

Paimon is a table format designed for realtime Lakehouse architectures, supporting unified streaming and batch workloads and integrating with engines like Flink and Spark. It offers transactional semantics, low-latency write paths, and optimized query performance for hybrid workloads.

Key features

  • Unified streaming and batch: Simplifies pipelines that require both real-time ingestion and analytical queries.
  • Transactional guarantees: Supports versioning and atomic operations to ensure consistency.
  • Multi-engine compatibility: Works with Flink, Spark and other ecosystem tools.
  • Active community and documentation for production adoption.

Use cases

  • Real-time analytics: Serve as a storage layer for low-latency ingestion and consistent queries.
  • Lakehouse modernization: Migrate data lakes to table formats that support streaming and batch workloads.

Technical highlights

  • Table-centric metadata and storage layout optimized for write amplification and read performance.
  • Tooling for data migration and version management to ease operations.

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Paimon
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