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Apache Allura 1.8.1 released

https://allura.apache.org/posts/2018-allura-1.8.1.html
Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2

Article Selections · ShardingSphere - Blog

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

v4.2.0-->

https://spark.apache.org/docs/latest/sql-performance-tuning.html
Leveraging Statistics Apache Spark’s ability to choose the best execution plan among many possible options is determined in part by its estimates of how many rows will be output by every node in the execution plan (read, filter, join, etc

🤔 Introducing APISIX AI Gateway – Built for LLMs and AI workloads.

https://apisix.apache.org/docs/apisix/plugins/traffic-split/
type[roundrobin, chash, ewma, least_conn]Algorithm for traffic splitting

Hadoop Auth – Hadoop Auth, Java HTTP SPNEGO - Server Side

https://hadoop.apache.org/docs/current/hadoop-auth/Configuration.html
Hadoop Auth – Hadoop Auth, Java HTTP SPNEGO - Server Side Configuration Wiki | git | Apache Hadoop | Last Published: 2026-03-24 | Version: 3

From Four Pipelines to One: How Tongcheng Travel Built a Unified Data

https://seatunnel.apache.org/blog/tongcheng-travel-unified-data-channel-with-apache-seatunnel
At an Apache SeaTunnel Meetup, Xiaochen Zhou, who works on the data platform at Tongcheng Travel, explained how the company consolidated those systems into a unified batch and streaming data channel based on the Apache SeaTunnel Zeta Engine

Apache Hudi vs Apache Iceberg for CDC Workloads

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

1. Setting up the environment

https://cayenne.apache.org/docs/4.0/getting-started-guide/
For an in-memory Derby database you can enter the following settings: JDBC Driver: org

The Apache Software Foundation Board of Directors Meeting Minutes March

https://apache.org/foundation/records/minutes/2009/board_minutes_2009_03_18.txt
The Apache Software Foundation Board of Directors Meeting Minutes March 18, 2009 1

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https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormatBase.html
@Public public abstract class TableInputFormatBase extends org

View TTL

https://phoenix.apache.org/docs/features/view-ttl
View TTL Per-view retention on top of a shared base table — different views can age data out on different schedules without splitting the table

Historical Phoenix performance comparisons and feature-level

https://phoenix.apache.org/docs/fundamentals/performance
2 improves both read and write performance by adding an extra hash byte at the start of the key and pre-splitting data into regions