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Either set it in the metastore's hive-site

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Schema Evolution

https://hudi.apache.org/docs/schema_evolution

Either set it in the metastore's hive-site

Using Apache Hudi with Apache Iceberg: Interoperability via Apache

https://hudi.apache.org/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable

Using Apache Hudi with Apache Iceberg: Interoperability via Apache XTable Tags: Yes — Apache Iceberg engines and catalogs can read Apache Hudi tables

Migrating from Parquet to Apache Hudi: A Practical Guide

https://hudi.apache.org/blog/2026/07/29/migrating-from-parquet-to-hudi

Migrating from Parquet to Apache Hudi: A Practical Guide Tags: You can migrate existing Parquet datasets to Apache Hudi either in place — using Hudi's bootstrap operation, which adds Hudi metadata without rewriting data files — or by rewriting the data through a one-time bulk insert

Ways to become a contributor

https://hudi.apache.org/contribute/how-to-contribute

org Report bugs or suggest improvements to the user experience; review code or RFCs on GitHub Share your success story on Hudi LinkedIn Community Syncs

Migrating from Apache Hive Tables to Apache Hudi

https://hudi.apache.org/blog/2026/07/30/migrating-from-hive-to-hudi

Migrating from Apache Hive Tables to Apache Hudi Tags: Migrating an Apache Hive table to Apache Hudi means converting its underlying data files into a Hudi table — either in place with Hudi's bootstrap mechanism or by rewriting the data with Spark — and re-registering it in your metastore through Hudi's catalog

Security Model

https://hudi.apache.org/contribute/security

If you have apprehensions regarding Hudi's security, or you discover vulnerability or potential threat, don’t hesitate to get in touch with the Apache Security Team by dropping a mail at security@apache

a GitHub account

https://hudi.apache.org/contribute/developer-setup

Once cloned, build Hudi by following Building Apache Hudi from source in the README, which also covers building against a specific Spark version or Flink version

Spark Quick Start

https://hudi.apache.org/docs/quick-start-guide

13 spark-shell --master "local[2]" \ --packages org

Change Data Capture with Debezium and Apache Hudi

https://hudi.apache.org/blog/2022/01/14/change-data-capture-with-debezium-and-apache-hudi

Change Data Capture with Debezium and Apache Hudi Tags: As of Hudi v0

Concurrency Control

https://hudi.apache.org/docs/concurrency_control

Hudi ensures atomic writes by publishing commits atomically to the timeline, stamped with an instant time that denotes when the action is deemed to have occurred

Migrating from Apache Iceberg to Apache Hudi

https://hudi.apache.org/blog/2026/08/05/migrating-from-apache-iceberg-to-hudi

Migrating from Apache Iceberg to Apache Hudi Tags: You can adopt Apache Hudi on an existing Apache Iceberg table either by translating its metadata with Apache XTable — no data rewrite, since both formats store data as Parquet files — or via a one-time rewrite; and because XTable also works in the reverse direction

Migrating from Delta Lake to Apache Hudi

https://hudi.apache.org/blog/2026/08/04/migrating-from-delta-lake-to-hudi

Migrating from Delta Lake to Apache Hudi Tags: You can migrate a Delta Lake table to Apache Hudi either by translating its metadata with Apache XTable — no data rewrite required, since both formats store data as Apache Parquet — or by rewriting the table once with a Spark bulk_insert; many teams start by running

Apache Hudi - The Data Lake Platform

https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform

Streaming: At its core, by optimizing for fast upserts & change streams, Hudi provides the primitives to data lake workloads that are comparable to what Apache Kafka does for event-streaming (namely, incremental produce/consume of events and a state-store for interactive querying)

Apache Hudi vs Delta Lake for Write-Heavy Workloads

https://hudi.apache.org/blog/2026/08/12/hudi-vs-delta-lake-for-write-heavy-workloads

Apache Hudi vs Delta Lake for Write-Heavy Workloads Tags: For update- and delete-heavy tables, the core architectural difference is this: Apache Hudi locates the records being changed through pluggable indexes — including a record-level index that maps each key to its file group — and can absorb changes into Merge

What is an Open Table Format?

https://hudi.apache.org/blog/2026/07/14/what-is-an-open-table-format

Apache Hudi, Apache Iceberg, Delta Lake, and Apache Paimon are the major open table formats; in all of them the data itself stays in open file formats like Apache Parquet or Apache ORC

What is Upsert on a Data Lake?

https://hudi.apache.org/blog/2026/07/15/what-is-upsert-on-a-data-lake

How Open Table Formats Make Upserts Possible Open table formats — Apache Hudi, Apache Iceberg, and Delta Lake — introduced a transactional metadata layer on top of immutable files, turning a directory of Parquet files into a proper table

Can a Lakehouse Really Run Maintenance Without Blocking Writes?

https://hudi.apache.org/blog/2026/07/17/async-compaction-background-maintenance

Tags: This is the fourth post in a series on Merge-On-Read as an architectural shift in Apache Hudi

What is CDC on a Data Lake?

https://hudi.apache.org/blog/2026/07/22/what-is-cdc-on-a-data-lake

Closing this gap is exactly why transactional table layers for the lake emerged; Apache Hudi, notably, grew out of the need to apply database change streams to lake storage at scale at Uber

What is Incremental ETL on a Data Lake?

https://hudi.apache.org/blog/2026/07/16/what-is-incremental-etl-on-a-data-lake

Apache Hudi supports both query types on Copy-on-Write and Merge-on-Read tables

Adopters

https://hudi.apache.org/powered-by

The following is a small sample of companies that have adopted or contributed to the Apache Hudi community

Apache Hudi vs Apache Iceberg for Streaming Ingestion

https://hudi.apache.org/blog/2026/08/11/hudi-vs-iceberg-for-streaming-ingestion

Apache Hudi vs Apache Iceberg for Streaming Ingestion Tags: Both Apache Hudi and Apache Iceberg can ingest streaming data, but they are built around different assumptions about how often a table is written to

Vendors

https://hudi.apache.org/vendors

Vendors are listed alphabetically; inclusion on this page is for the convenience of the community and does not imply endorsement by the Apache Software Foundation or the Apache Hudi project

What is ACID on a Data Lake?

https://hudi.apache.org/blog/2026/07/17/what-is-acid-on-a-data-lake

This post walks through what each of the four letters means concretely at the file level, how lakes worked before table formats existed, how formats like Apache Hudi deliver these guarantees, and — just as important — what ACID on a lake does not promise

Overview

https://hudi.apache.org/docs/overview

This overview will provide a high level summary of what Apache Hudi is and will orient you on how to learn more to get started

Data Lakehouse vs Data Warehouse vs Data Lake: What's the Difference?

https://hudi.apache.org/blog/2026/07/23/lakehouse-vs-data-warehouse-vs-data-lake

Projects like Apache Hudi, Apache Iceberg, and Delta Lake define tables (not just files) over Parquet and ORC, and bring the database capabilities the lake was missing: ACID transactions with concurrency control, so writers commit atomically and readers never see partial results