What is Hudi?

Hudi (short for “Hadoop Upserts Deletes and Incrementals”) is an open-source framework that sits on top of data-lake storage (like Amazon S3 or HDFS) and lets you easily add, change, or delete large batches of data. It gives you fast, incremental reads and writes while keeping the data consistent, similar to a database but for big-data files.

Let's break it down

  • Open-source: Free for anyone to use, modify, and share.
  • Framework: A set of tools and libraries that help you build something bigger, in this case a data-lake system.
  • Data lake: A huge storage area where raw data (logs, events, files) is kept in its original format.
  • Upserts: A combination of “update” and “insert” - if a record exists it’s changed, otherwise a new one is added.
  • Deletes: Removing records that are no longer needed.
  • Incremental reads: Pulling only the data that changed since the last read, instead of scanning everything again.
  • Apache Spark: A popular engine for processing big data; Hudi uses Spark to run its jobs.
  • Cloud storage (S3, HDFS, etc.): The place where the raw files live; Hudi works directly on these files.

Why does it matter?

Because modern companies collect massive streams of data, they need a way to keep that data clean, up-to-date, and queryable without rebuilding the whole lake every night. Hudi gives you near-real-time data freshness, lower storage costs, and simpler pipelines, which translates into faster insights and cheaper operations.

Where is it used?

  • Uber uses Hudi to manage billions of trip events, allowing quick updates and real-time dashboards.
  • Alibaba applies Hudi for its e-commerce click-stream, enabling incremental analytics for product recommendations.
  • Netflix stores streaming-service logs in Hudi tables to run daily quality checks and rapid A/B test analysis.
  • A large bank leverages Hudi for fraud-detection feeds, updating risk scores as new transactions arrive.

Good things about it

  • Provides ACID-like transactions on top of immutable file storage.
  • Supports upserts and deletes, which many plain data-lake formats lack.
  • Enables incremental queries, dramatically reducing read time for fresh data.
  • Integrates smoothly with Spark, Hive, Presto, and other big-data tools.
  • Backed by an active open-source community and major contributors (e.g., Apache, Uber).

Not-so-good things

  • Requires a Spark (or Flink) cluster, adding operational complexity for small teams.
  • Learning curve can be steep for users new to versioned data-lake concepts.
  • Write performance may be slower than raw file writes because of indexing and commit handling.
  • Limited native support for non-Hadoop storage systems (e.g., Azure Data Lake Gen2 needs extra configuration).