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| <strong>Apache Flink<sup>®</sup> — Stateful Computations over Data Streams</strong> |
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| <dt> <a href="/news/2020/08/20/flink-docker.html">The State of Flink on Docker</a></dt> |
| <dd>This blog post gives an update on the recent developments of Flink's support for Docker.</dd> |
| |
| <dt> <a href="/2020/08/19/statefun.html">Monitoring and Controlling Networks of IoT Devices with Flink Stateful Functions</a></dt> |
| <dd><p>In this blog post, we’ll take a look at a class of use cases that is a natural fit for <a href="https://flink.apache.org/stateful-functions.html">Flink Stateful Functions</a>: monitoring and controlling networks of connected devices (often called the “Internet of Things” (IoT)).</p> |
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| </dd> |
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| <dt> <a href="/news/2020/08/06/external-resource.html">Accelerating your workload with GPU and other external resources</a></dt> |
| <dd>This post introduces the new External Resource Framework in Flink 1.11 and take GPU as an example to show how to accelerate your workload with external resources.</dd> |
| |
| <dt> <a href="/2020/08/04/pyflink-pandas-udf-support-flink.html">PyFlink: The integration of Pandas into PyFlink</a></dt> |
| <dd>The Apache Flink community put some great effort into integrating Pandas with PyFlink in the latest Flink version 1.11. Some of the added features include support for Pandas UDF and the conversion between Pandas DataFrame and Table. In this article, we will introduce how these functionalities work and how to use them with a step-by-step example.</dd> |
| |
| <dt> <a href="/news/2020/07/30/demo-fraud-detection-3.html">Advanced Flink Application Patterns Vol.3: Custom Window Processing</a></dt> |
| <dd>In this series of blog posts you will learn about powerful Flink patterns for building streaming applications.</dd> |
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