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commit 177fe4fbe3028b2b9f9ff00e56ce15665ef4a880 Author: Alexander Fedulov <[email protected]> AuthorDate: Thu Jul 2 14:38:40 2020 +0200 Link blogposts This closes #354. --- _posts/2020-01-15-demo-fraud-detection.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2020-01-15-demo-fraud-detection.md b/_posts/2020-01-15-demo-fraud-detection.md index 96a3c27..291dde7 100644 --- a/_posts/2020-01-15-demo-fraud-detection.md +++ b/_posts/2020-01-15-demo-fraud-detection.md @@ -13,7 +13,7 @@ excerpt: In this series of blog posts you will learn about three powerful Flink In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications: - - Dynamic updates of application logic + - [Dynamic updates of application logic]({{ site.baseurl }}/news/2020/03/24/demo-fraud-detection-2.html) - Dynamic data partitioning (shuffle), controlled at runtime - Low latency alerting based on custom windowing logic (without using the window API) @@ -219,4 +219,4 @@ In the second part of this series, we will describe how the rules make their way </center> <br/> -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). +In the [next article]({{ site.baseurl }}/news/2020/03/24/demo-fraud-detection-2.html), 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).
