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Either set it in the metastore's hive-site
from the top result below
https://hudi.apache.org/docs/schema_evolution
Either set it in the metastore's hive-site
https://hudi.apache.org/docs/quick-start-guide
NOTE:
For users who have Spark-Hive integration in their environment, this guide assumes that you have the appropriate settings configured to allow Spark to create tables and register in Hive Metastore
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
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/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/contribute/developer-setup
for eg, if you have been working with Hudi Streamer, you can build using this command instead of entire project
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/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/2026/08/04/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/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/2026/08/12/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/2026/07/15/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/2026/07/16/what-is-incremental-etl-on-a-data-lake
Apache Hudi supports both query types on Copy-on-Write and Merge-on-Read tables
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/2021/07/21/streaming-data-lake-platform
Through open sourcing and graduating from the Apache Incubator, we have made some great progress elevating these ideas across the industry, as well as bringing them to life with a cohesive software stack
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
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/2026/07/23/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/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/2026/07/17/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/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/learn/tech-specs
Actions
Actions have a plan (optional) and completion metadata associated with them that capture how the action alters the table state
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/vendors
Vendors
The following vendors provide managed services, cloud platform integrations, or commercial support for 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