After careful consideration, we have decided to discontinue Amazon Kinesis Data Analytics for SQL applications:

1. From **September 1, 2025**, we won't provide any bug fixes for Amazon Kinesis Data Analytics for SQL applications because we will have limited support for it, given the upcoming discontinuation.

2. From **October 15, 2025**, you will not be able to create new Kinesis Data Analytics for SQL applications.

3. We will delete your applications starting **January 27, 2026**. You will not be able to start or operate your Amazon Kinesis Data Analytics for SQL applications. Support will no longer be available for Amazon Kinesis Data Analytics for SQL from that time. For more information, see [Amazon Kinesis Data Analytics for SQL Applications discontinuation](discontinuation.md).

# Example: Detecting Data Anomalies and Getting an Explanation (RANDOM\$1CUT\$1FOREST\$1WITH\$1EXPLANATION Function)
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Amazon Kinesis Data Analytics provides the `RANDOM_CUT_FOREST_WITH_EXPLANATION` function, which assigns an anomaly score to each record based on values in the numeric columns. The function also provides an explanation of the anomaly. For more information, see [RANDOM\$1CUT\$1FOREST\$1WITH\$1EXPLANATION](https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sqlrf-random-cut-forest-with-explanation.html) in the *Amazon Managed Service for Apache Flink SQL Reference*. 

In this exercise, you write application code to obtain anomaly scores for records in your application's streaming source. You also obtain an explanation for each anomaly.

**Topics**
+ [Step 1: Prepare the Data](app-anomaly-with-ex-prepare.md)
+ [Step 2: Create an Analytics Application](app-anom-with-exp-create-app.md)
+ [Step 3: Examine the Results](examine-results-with-exp.md)

**First Step**  
[Step 1: Prepare the Data](app-anomaly-with-ex-prepare.md)