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commit 55b6c7c4379ca3ba6dfca5b720c4aa167ab4f779 Author: Nico Kruber <[email protected]> AuthorDate: Tue Jul 28 16:52:43 2020 +0200 Rebuild website --- content/blog/feed.xml | 6 +++--- content/news/2020/01/15/demo-fraud-detection.html | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/content/blog/feed.xml b/content/blog/feed.xml index a77152d..4f96e80 100644 --- a/content/blog/feed.xml +++ b/content/blog/feed.xml @@ -13,7 +13,7 @@ <p>In the following sections, we describe how to integrate Kafka, MySQL, Elasticsearch, and Kibana with Flink SQL to analyze e-commerce user behavior in real-time. All exercises in this blogpost are performed in the Flink SQL CLI, and the entire process uses standard SQL syntax, without a single line of Java/Scala code or IDE installation. The final result of this demo is shown in the following figure:</p> <center> -<img src="/img/blog/2020-05-03-flink-sql-demo/image1.gif" width="650px" alt="Demo Overview" /> +<img src="/img/blog/2020-07-28-flink-sql-demo/image1.gif" width="650px" alt="Demo Overview" /> </center> <p><br /></p> @@ -5125,7 +5125,7 @@ However, you need to take care of another aspect, which is providing timestamps <description><p>In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications:</p> <ul> - <li>Dynamic updates of application logic</li> + <li><a href="/news/2020/03/24/demo-fraud-detection-2.html">Dynamic updates of application logic</a></li> <li>Dynamic data partitioning (shuffle), controlled at runtime</li> <li>Low latency alerting based on custom windowing logic (without using the window API)</li> </ul> @@ -5325,7 +5325,7 @@ To understand why this is the case, let us start with articulating a realistic s </center> <p><br /></p> -<p>In the next article, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p> +<p>In the <a href="/news/2020/03/24/demo-fraud-detection-2.html">next article</a>, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p> </description> <pubDate>Wed, 15 Jan 2020 13:00:00 +0100</pubDate> <link>https://flink.apache.org/news/2020/01/15/demo-fraud-detection.html</link> diff --git a/content/news/2020/01/15/demo-fraud-detection.html b/content/news/2020/01/15/demo-fraud-detection.html index 22fe277..dcb51b4 100644 --- a/content/news/2020/01/15/demo-fraud-detection.html +++ b/content/news/2020/01/15/demo-fraud-detection.html @@ -200,7 +200,7 @@ <p>In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications:</p> <ul> - <li>Dynamic updates of application logic</li> + <li><a href="/news/2020/03/24/demo-fraud-detection-2.html">Dynamic updates of application logic</a></li> <li>Dynamic data partitioning (shuffle), controlled at runtime</li> <li>Low latency alerting based on custom windowing logic (without using the window API)</li> </ul> @@ -400,7 +400,7 @@ To understand why this is the case, let us start with articulating a realistic s </center> <p><br /></p> -<p>In the next article, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p> +<p>In the <a href="/news/2020/03/24/demo-fraud-detection-2.html">next article</a>, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p> </article> </div>
