Example: Detecting Data Anomalies and Getting an Explanation (RANDOM_CUT_FOREST_WITH_EXPLANATION Function)
For new projects, we recommend that you use the new Kinesis Data Analytics Studio over Kinesis Data Analytics for SQL Applications. Kinesis Data Analytics Studio combines ease of use with advanced analytical capabilities, enabling you to build sophisticated stream processing applications in minutes.
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_CUT_FOREST_WITH_EXPLANATION in the
Amazon Kinesis Data Analytics 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.
First Step
