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That is precisely what Apache XTable does
from the top result below
https://hudi.apache.org/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable
That is precisely what Apache XTable does
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/2026/08/12/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/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/blog/2026/07/14/what-is-an-open-table-format
The designs are converging here: proposals in the Iceberg community for format version 4 — single-file commits and an adaptive root manifest — would move its commit metadata toward the log-structured approach Hudi's timeline and Delta's transaction log have used from the beginning, though these are not yet part of
https://hudi.apache.org/blog/2026/07/22/what-is-cdc-on-a-data-lake
Doing this efficiently means quickly finding which file holds a given key — Hudi does so with pluggable indexes, including a record-level index, so it does not have to scan the table on every write
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/2026/07/17/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/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/learn/tech-specs
├── [data_files]
Data files can be either base files or log files
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/docs/concurrency_control
Add the following Maven packages (check the latest versions at time of install):
com
https://hudi.apache.org/blog/2026/08/05/migrating-from-apache-iceberg-to-hudi
"Migrating from Iceberg to Hudi" does not have to mean a hard cutover of every writer, reader, catalog entry and dashboard on the same weekend
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/2026/07/15/what-is-upsert-on-a-data-lake
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An upsert is a write operation that updates a record if it already exists and inserts it if it does not — a single atomic operation that combines update and insert, keyed on a unique record identifier
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/2026/07/16/what-is-incremental-etl-on-a-data-lake
The pipeline's work is proportional to how much the data changed, not how large the tables have grown
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/contribute/security
In real-world deployments, engines like Presto, Apache Spark, Apache Flink are hosted in private virtual network—such as a VPC, VLAN, or on-premises subnet—where only trusted entities have access and network controls including firewalls, ACLs, and routing rules are used to restrict and prevent untrusted access
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/quick-start-guide
13
spark-shell --master "local[2]" \
--packages org
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/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/2026/08/06/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/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/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/2026/07/21/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/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