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Apache Hudi

Apache Hudi is an open-source data management framework (data lakehouse / data management) for streaming data lakes on top of distributed storage.

  • Incremental data processing and ingestion into data lakes (data engineering)
  • Support for upserts, deletes, and change capture on large analytic tables (data lakehouse)
  • Storage management with copy-on-write and merge-on-read table formats (data storage)
  • Query integration with compute engines such as Apache Spark, Presto, Trino, and Apache Hive (data analytics)
  • Table services for compaction, cleaning, clustering, and indexing (data operations)

More About Apache Hudi

Apache Hudi is a data management framework (data lakehouse) that adds transactional and incremental processing capabilities to data lakes built on distributed storage. It is designed for managing large analytical datasets where data arrives continuously and needs to be ingested, updated, and queried efficiently using existing big data processing engines.

The project addresses the problem of managing mutable datasets on data lakes, where traditional file-based layouts are optimized mainly for batch append-only workloads. Hudi introduces a table abstraction that organizes data on storage while providing capabilities such as upserts, deletes, and change streams. This enables use cases like maintaining serving-ready analytical tables, building Change Data Capture (CDC) pipelines, and supporting near real-time data warehousing patterns.

Apache Hudi supports two primary storage table types (data storage): Copy-on-Write (COW) and Merge-on-Read (MOR). Copy-on-Write tables rewrite files on each commit and provide snapshot queries directly from columnar files. Merge-on-Read tables store a combination of base files and incremental log files and support near real-time queries by merging base and log data at read time. These approaches allow enterprises to choose between write performance and read performance trade-offs depending on workloads.

The framework integrates with widely used processing engines (data analytics), including Apache Spark for ingestion, transformation, and table services, and query engines such as Apache Hive, Presto, and Trino for analytical queries. Hudi exposes tables through metadata and layouts that are compatible with these engines, enabling enterprises to keep existing query tools while adding record-level mutation and incremental data handling.

Apache Hudi provides table services (data operations) that manage the lifecycle of data on storage. These services include compaction for Merge-on-Read tables, cleaning of obsolete files, clustering for data layout optimization, indexing for efficient record location, and archiving of metadata. These operations can run in the background or as scheduled jobs, allowing teams to control storage costs and query performance.

In enterprise environments, Apache Hudi is used as a storage and data ingestion layer (data engineering) that maintains large analytic tables on object stores or distributed file systems while enabling streaming or micro-batch ingestion patterns. It can ingest data from streaming sources, apply incremental processing, and expose tables for batch and interactive analytics. This supports architectures where a data lake serves as the central repository for both historical and near real-time data.

Within a technical taxonomy, Apache Hudi fits into data lakehouse storage, streaming data ingestion, and big data table management categories. It operates alongside compute engines, schedulers, and metadata catalogs, and focuses on how data is written, stored, and maintained over time. Its capabilities target organizations that require transactional semantics, incremental pipelines, and operational controls over large analytical datasets on cloud or on-premises (on-prem) storage.