infoverload commented on a change in pull request #468:
URL: https://github.com/apache/flink-web/pull/468#discussion_r716718876
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File path: _posts/2021-09-21-release-1.14.0.md
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@@ -0,0 +1,274 @@
+---
+layout: post
+title: "Apache Flink 1.14.0 Release Announcement"
+date: 2021-09-21T08:00:00.000Z
+categories: news
+authors:
+- joemoe:
+ name: "Johannes Moser"
+
+excerpt: The Apache Flink community is excited to announce the release of
Flink 1.14.0! Around xxx contributors worked on over xxxx issues to TODO.
+---
+
+Just a couple of days ago the Apache Software Foundation announced its annual
report and Apache
+Flink was again in the Top 5 of the most active projects in all relevant
categories. This remarkable
+activity is also reflected in this new 1.14.0 release. Once again, more than
200 contributors worked on
+over 1,000 issues. We are proud of how this community is consistently moving
the project forward.
+
+The release brings many cool improvements, from SQL to connectors,
checkpointing, and PyFlink.
+A big area of changes in this release is the integrated streaming & batch
experience. We believe
+that unbounded stream processing goes hand-in-hand with bounded- and batch
processing tasks in practice.
+Data exploration when developing new applications, bootstrapping state for new
applications, training
+models to be applied in the streaming application, re-processing data after
fixes/upgrades, and many
+other use cases require processing historic data from various sources next to
the streaming data.
Review comment:
```suggestion
This release brings many new features and improvements in areas such as the
SQL API, more connector support, checkpointing, and PyFlink. But most notable
are the changes to the integrated streaming and batch experience. We believe
that, in practice, unbounded stream processing should work seamlessly with
bounded processing and batch processing tasks. Many use cases require
processing historic data from various sources alongside streaming data (i.e.
data exploration when developing new applications, bootstrapping state for new
applications, training models to be applied in the streaming application,
re-processing data after fixes/upgrades).
```
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