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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
https://hudi.apache.org/docs/schema_evolution
Either set it in the metastore's hive-site
https://hudi.apache.org/contribute/developer-setup
a GitHub account
a Linux (or) macOS development environment with Java JDK 11, Apache Maven (3
https://hudi.apache.org/blog/2022/01/14/change-data-capture-with-debezium-and-apache-hudi
Change Data Capture with Debezium and Apache Hudi
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As of Hudi v0
https://hudi.apache.org/docs/concurrency_control
Add the following Maven packages (check the latest versions at time of install):
com
https://hudi.apache.org/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable
Apache XTable (incubating) translates Hudi table metadata into Apache Iceberg (and Delta Lake) metadata in place, so a single copy of data on object storage is readable as any of the three formats
https://hudi.apache.org/blog/2026/08/05/migrating-from-apache-iceberg-to-hudi
Migrating from Apache Iceberg to Apache Hudi
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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
https://hudi.apache.org/blog/2026/08/04/migrating-from-delta-lake-to-hudi
Migrating from Delta Lake to Apache Hudi
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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
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)
https://hudi.apache.org/contribute/security
Security Model
Apache Hudi is a library that relies on the security posture of the underlying compute engine and storage environment in which it operates
https://hudi.apache.org/powered-by
Adopters
Apache Hudi is a fast growing diverse community of people and organizations from all around the globe
https://hudi.apache.org/blog/2026/07/30/migrating-from-hive-to-hudi
Migrating from Apache Hive Tables to Apache Hudi
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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
https://hudi.apache.org/blog/2026/07/29/migrating-from-parquet-to-hudi
The mechanics of skeleton files are covered in the original deep-dive, Efficient Migration of Large Parquet Tables to Apache Hudi
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
https://hudi.apache.org/blog/2026/07/14/what-is-an-open-table-format
The Problem Before Table Formats
Until roughly a decade ago, the de-facto "table" abstraction on data lakes was the Apache Hive model: a table was a directory, partitions were subdirectories, and the Hive Metastore recorded the schema and the list of partitions
https://hudi.apache.org/docs/quick-start-guide
13
spark-shell --master "local[2]" \
--packages org
https://hudi.apache.org/blog/2020/08/18/hudi-incremental-processing-on-data-lakes
cn article, found here, with minor edits
Throughout the development of big data technology, Hadoop has steadily seized the opportunities of this era and has become the de-facto standard for enterprises to build big data infrastructure
https://hudi.apache.org/blog/2026/07/17/async-compaction-background-maintenance
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This is the fourth post in a series on Merge-On-Read as an architectural shift in Apache Hudi
https://hudi.apache.org/learn/tech-specs-0-point-x
Broadly, there can be two types of data files
Base files - Files that contain a set of records in columnar file formats like Apache Parquet/Orc or indexed formats like HFile format
https://hudi.apache.org/blog/2026/07/16/what-is-incremental-etl-on-a-data-lake
Organizations routinely spend a large share of their data platform budget on pipelines that mostly reprocess data they already processed yesterday
https://hudi.apache.org/blog/2026/08/06/hudi-vs-iceberg-for-cdc-workloads
The same multi-modal indexing subsystem extends beyond keys — secondary indexes and expression indexes on non-key columns, borrowed straight from relational database design — which is one of the deeper architectural choices catalogued in 21 unique differentiators of Apache Hudi
https://hudi.apache.org/blog/2026/08/11/hudi-vs-iceberg-for-streaming-ingestion
Apache Hudi vs Apache Iceberg for Streaming Ingestion
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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
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
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
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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
https://hudi.apache.org/blog/2026/07/23/lakehouse-vs-data-warehouse-vs-data-lake
A data lake is cheap, open storage — typically cloud object stores holding raw files of any shape, from CSVs to images — that scales practically without limit but offers few database guarantees
https://hudi.apache.org/blog/2026/07/21/what-is-a-streaming-data-lake
This is why indexing sits in the write path of Apache Hudi rather than being only a query-side optimization
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
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
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
https://hudi.apache.org/docs/indexes
Given that Hudi’s design has been heavily optimized for handling mutable change streams, with different write patterns, Hudi considers indexing as an integral part of its design and has uniquely supported indexing capabilities from its inception, to speed up writes on the data lakehouse, while still providing