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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
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Yes — Apache Iceberg engines and catalogs can read Apache Hudi tables
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
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/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://doris.apache.org
LightningFastAnalytics andSearch Database
Apache Doris is an open-source, real-time database for modern analytics and AI
https://hudi.apache.org/docs/quick-start-guide
13
spark-shell --master "local[2]" \
--packages org
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/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://news.apache.org/?p=7621
org blog about why the rebuild mattered, what went into it, and how you can get involved
https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform
Apache Hudi - The Data Lake Platform
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Apache Hudi is a streaming data lake platform built around a database kernel—not merely a table format or a transactional layer, but a complete software stack with built-in table services, data services, and incremental processing capabilities
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/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/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/2026/08/05/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://fluss.apache.org
Data continuously tiers down to a Lakehouse cold tier (Apache Paimon, Apache Iceberg, Apache Hudi, Lance) via a Tiering Service
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/vendors
Vendors
The following vendors provide managed services, cloud platform integrations, or commercial support for Apache Hudi
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
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/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/2026/07/17/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/2026/07/21/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/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/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://www.apache.org/
Taking place on October 10, the event is dedicated to all things related to open lakehouse technologies under the Apache Software Foundation (ASF) umbrella, with content supporting Apache Iceberg™, Apache Hudi, Apache Parquet™, Apache Gravitino, Apache Polaris™, and more
https://kyuubi.apache.org
Data Lake & Lakehouses
Kyuubi supports query all traditional data warehouses, like Apache Hive/HDFS, or modern lakehouses, like Apache Iceberg, Apache Hudi, and Delta Lake, together
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
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/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