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https://hudi.apache.org/contribute/how-to-contribute
Ways to become a contributor
A GitHub account is needed to file issues, start discussions, and send pull requests to Hudi
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/contribute/developer-setup
a GitHub account
a Linux (or) macOS development environment with Java JDK 11, Apache Maven (3
https://hudi.apache.org/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable
First, get the XTable utilities jar — build it from source or download it from the project's GitHub packages
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
Migrating from Parquet to Apache Hudi: A Practical Guide
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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
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
https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform
We consciously introduced two table types Copy On Write (with simpler operability) and Merge On Read (for greater flexibility) and now these terms are used in projects outside Hudi, to refer to similar ideas being borrowed from Hudi
https://hudi.apache.org/powered-by
The following is a small sample of companies that have adopted or contributed to the Apache Hudi community
https://hudi.apache.org/blog/2026/08/06/hudi-vs-iceberg-for-cdc-workloads
Apache Hudi vs Apache Iceberg for CDC Workloads
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Both Apache Hudi and Apache Iceberg can ingest change data capture (CDC) streams, but they were designed around different problems
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/2026/08/11/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
Onehouse offers commercial support for Apache Hudi and contributes heavily to its development
https://hudi.apache.org/docs/schema_evolution
Either set it in the metastore's hive-site
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/2026/07/14/what-is-an-open-table-format
Apache Iceberg arrived independently from Netflix's need for correct, scalable table metadata, and positioned itself squarely around the table format core: the specification and libraries, with maintenance left to engines
https://hudi.apache.org/docs/quick-start-guide
Using Spark Datasource APIs(both scala and python) and using Spark SQL, we will walk through code snippets that allows you to insert, update, delete and query a Hudi table
https://hudi.apache.org/blog/2022/01/14/change-data-capture-with-debezium-and-apache-hudi
Design Overview
The architecture for an end-to-end CDC ingestion flow with Apache Hudi is shown above
https://hudi.apache.org/blog/2026/07/23/lakehouse-vs-data-warehouse-vs-data-lake
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A data warehouse is a proprietary, structured, SQL-first analytical database: you load cleaned, modeled data into it and get fast, governed BI queries out
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/2026/07/17/what-is-acid-on-a-data-lake
How Table Formats Deliver ACID
The shared insight behind Apache Hudi, Apache Iceberg and Delta Lake is that the table's state should be defined by a metadata log with an atomic publish step, not by whatever files happen to be in a directory
https://hudi.apache.org/blog/2026/07/21/what-is-a-streaming-data-lake
This post is the definitional companion to the original 2021 essay, Apache Hudi - The Data Lake Platform, which first articulated this vision in depth
https://hudi.apache.org/blog/2020/08/18/hudi-incremental-processing-on-data-lakes
Apache Hudi is a data lake framework which provides exactly this ability to ingest, manage and query large analytical data sets on a distributed file system/cloud stores
https://hudi.apache.org/blog/2026/07/15/what-is-upsert-on-a-data-lake
Why Data Lakes Couldn't Update Records
Data lakes are built on files — typically columnar formats like Apache Parquet or ORC — sitting on distributed file systems or cloud object storage such as Amazon S3
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
Apache Hudi will manage metadata, and provide common abstractions and pluggable interfaces to most/all common compute/query engines
https://hudi.apache.org/blog/2026/07/22/what-is-cdc-on-a-data-lake
Architecturally, data stored in open file formats like Apache Parquet on object storage is not locked to any one engine
https://hudi.apache.org/docs/indexes
g: Apache Hive/Apache Iceberg) end up having to merge all the base files against all incoming updates/delete records (10-100x more read amplification)
https://hudi.apache.org/blog/2026/07/16/what-is-incremental-etl-on-a-data-lake
Each run of an incremental pipeline consumes a delta — the records inserted, updated, or deleted after a known checkpoint — applies its transformation logic to just that delta, and merges the results into the target table