Web This site Trusted
30 result(s) for “okapi bm25 site who unesco org site does apache org site have apache org site:hudi.apache.org” of 31 matched · 490 ms
Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable
Using Apache Hudi with Apache Iceberg: Interoperability via Apache XTable
Tags:
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
Tags:
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/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/2026/07/30/migrating-from-hive-to-hudi
Migrating from Apache Hive Tables to Apache Hudi
Tags:
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/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/contribute/developer-setup
5
To filter particular scala test:
mvn -Dsuites="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
Tags:
As of Hudi v0
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/2026/08/12/hudi-vs-delta-lake-for-write-heavy-workloads
Apache Hudi vs Delta Lake for Write-Heavy Workloads
Tags:
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
Tags:
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
Tags:
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/2026/08/11/hudi-vs-iceberg-for-streaming-ingestion
Apache Hudi vs Apache Iceberg for Streaming Ingestion
Tags:
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/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/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/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/2026/07/17/async-compaction-background-maintenance
Tags:
This is the fourth post in a series on Merge-On-Read as an architectural shift in Apache Hudi
https://hudi.apache.org/blog/2026/07/22/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/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/2026/07/16/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/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/2026/07/17/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/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/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
Syntax
note
This document specifies the current storage format — table version 9, as shipped through Hudi 1
https://hudi.apache.org/blog/2026/08/06/hudi-vs-iceberg-for-cdc-workloads
Apache Hudi vs Apache Iceberg for CDC Workloads
Tags:
Both Apache Hudi and Apache Iceberg can ingest change data capture (CDC) streams, but they were designed around different problems