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https://hop.apache.org/blog/2026/06/hop-2.18.1/
The Apache Hop community is excited to announce the release of Apache Hop 2
https://tsfile.apache.org/zh/
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https://shardingsphere.apache.org/legacy.html
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Copyright © 2018-2023, Apache ShardingSphere, ShardingSphere, Apache, the Apache feather logo, and the Apache ShardingSphere project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries
https://hop.apache.org/blog/2026/08/hop-2.19.0/
The Apache Hop community is happy to announce the release of Apache Hop 2
https://bifromq.apache.org/blog/announcing-apache-bifromq-4-0-0-incubating/
We are pleased to announce the availability of Apache BifroMQ 4
https://attic.apache.org/resolution.html
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Search The Apache Attic
Board Resolution to move to the Attic
The following is a sample resolution to move a project to the Apache Attic. A project’s chair should include this in the agenda for the next board meeting:
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https://yetus.apache.org/documentation/history/
The
purpose was to run a few basic tests against the Apache Hadoop source tree so that developers
would not have to re-run common tests before trying to merge a commit
https://shardingsphere.apache.org/blog/en/material
To support customized chaos engineering, the ShardingSphere-on-Cloud project is designing and imple
How to Run SQL Trace with ShardingSphere-Agent
Apache ShardingSphere, a data service platform that follows the Database Plus concept for distributed database systems, offers a range of features, including data sharding
https://stormcrawler.apache.org/docs/
A: If you’re new to these concepts, it’s worth starting with Apache Storm®
https://xtable.apache.org
Apache XTable™ provides abstraction interfaces that allow omni-directional interoperability across Delta, Hudi, Iceberg, and any other future lakehouse table formats such as Apache Paimon
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/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
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/docs/quick-start-guide
13
spark-shell --master "local[2]" \
--packages org
https://hudi.apache.org/contribute/developer-setup
Once cloned, build Hudi by following Building Apache Hudi from source in the README, which also covers building against a specific Spark version or Flink version
https://hudi.apache.org/docs/concurrency_control
Concurrency Control
Concurrency control defines how different writers, readers, and table services coordinate access to a Hudi table
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
**
Community
Apache Iceberg has quickly become a foundational technology in modern data architectures, but its impact goes far beyond performance and scale
https://apache.org/foundation/records/minutes/2022/board_minutes_2022_09_21.txt
Apache Hudi Project [Vinoth Chandar / Sam] See Attachment AK AL
https://apache.org/foundation/records/minutes/2020/board_minutes_2020_06_17.txt
The Apache Software Foundation Board of Directors Meeting Minutes June 17, 2020 1
https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/Table.html
Service, R>
Map<byte[],R>
default <T extends org
https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/client/Admin.html
IOException - if a remote or network exception occurs
createTableAsync
Creates a new table but does not block and wait for it to come online
https://apache.org/foundation/records/minutes/2019/board_minutes_2019_04_17.txt
The Apache Software Foundation Board of Directors Meeting Minutes April 17, 2019 1
https://apache.org/foundation/records/minutes/2018/board_minutes_2018_09_19.txt
Special thanks to: * Rich Bowen for coordinating * Ruth Suehle, for "everything" (Ruth is bridging the gap between producers) * Kevin McGrail, for fundraising * Sally Khudairi, for marketing assistance * Sharan Foga, for organizing the ASF booth presence * Trevor Grant, for being our on-site person for the booth *
https://apache.org/foundation/records/minutes/2017/board_minutes_2017_03_15.txt
The Apache Software Foundation Board of Directors Meeting Minutes March 15, 2017 1
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/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/2026/08/12/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