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Apache Hudi

A storage and incremental processing framework for big data that supports upserts, deletes and incremental consumption for real-time and batch analytics.

Apache Hudi is a storage and incremental processing framework for big data that enables upserts, deletes, and incremental consumption to support both real-time and batch analytics. It offers indexing, write-path optimizations, and version management to reduce processing latency and improve storage efficiency.

Key features

  • Incremental processing and upserts: Merge writes for handling change data capture (CDC) and mutable datasets.
  • Versioning and indexing: Built-in index and metadata management for efficient reads and time-travel queries.
  • Multi-engine compatibility: Works with Spark, Presto, Trino and other analytics engines.
  • Production-ready ecosystem: Documentation and deployment patterns for production environments.

Use cases

  • Real-time ETL and CDC: Serve as a data layer that enables near-real-time synchronization and analytics.
  • Data governance and compliance: Track data history and metadata for auditability.

Technical highlights

  • Focused on write-optimized strategies and indexing to reduce write amplification and improve read performance.
  • Tooling to support migration from traditional data lakes or warehouses to Hudi.

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Apache Hudi
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