Hudi is a data lake framework that enables upserts, deletes, and incremental processing on big data stored in cloud object stores and distributed file systems.
Hudi addresses the challenge of efficiently updating and deleting records in large-scale data lakes, which traditionally support only immutable append-only writes. It provides a table abstraction layer that manages data organization and versioning, allowing applications to perform record-level updates and deletes while maintaining data integrity. The framework tracks changes incrementally, enabling downstream systems to consume only the data that has changed rather than reprocessing entire datasets. This approach works by organizing data into small immutable files with metadata that tracks which records have been modified, deleted, or added.
Hudi suits organizations building data lakes that require frequent updates to historical records or need to synchronize data across multiple systems. It works well for use cases like change data capture pipelines, data warehouse ingestion, and maintaining slowly-changing dimensions. Teams should adopt it when their data pipeline requires both high-throughput writes and the ability to correct or update records after initial ingestion. The framework integrates with Apache Spark and Apache Flink for processing, allowing existing big data workflows to incorporate Hudi's capabilities without complete rewrites.
The project maintains active development across its core functionality and integrations. Work spans multiple areas including enhancements to the table format and storage layer, improvements to query performance and incremental processing capabilities, and expansion of ecosystem integrations. The codebase receives regular updates addressing both new features and operational stability. Community contributions flow steadily into the project, with ongoing refinement of APIs and internal mechanisms to support evolving use cases in data lake management.
