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).

# Step 2: Create an Analytics Application


In this section, you create an Amazon Kinesis Data Analytics application and configure it to use the Kinesis data stream that you created as the streaming source in [Step 1: Prepare the Data](app-anomaly-with-ex-prepare.md). You then run application code that uses the `RANDOM_CUT_FOREST_WITH_EXPLANATION` function.

**To create an application**

1. Open the Kinesis console at [https://console.amazonaws.cn/kinesis](https://console.amazonaws.cn/kinesis).

1. Choose **Data Analytics** in the navigation pane, and then choose **Create application**.

1. Provide an application name and description (optional), and choose **Create application**.

1. Choose **Connect streaming data**, and then choose **ExampleInputStream** from the list. 

1. Choose **Discover schema**, and make sure that `Systolic` and `Diastolic` appear as `INTEGER` columns. If they have another type, choose **Edit schema**, and assign the type `INTEGER` to both of them. 

1. Under **Real time analytics**, choose **Go to SQL editor**. When prompted, choose to run your application. 

1. Paste the following code into the SQL editor, and then choose **Save and run SQL**.

   ```
   --Creates a temporary stream.
   CREATE OR REPLACE STREAM "TEMP_STREAM" (
   	        "Systolic"                  INTEGER,
   	        "Diastolic"                 INTEGER,
   	        "BloodPressureLevel"        varchar(20),
   	        "ANOMALY_SCORE"             DOUBLE,
   	        "ANOMALY_EXPLANATION"       varchar(512));
   
   --Creates another stream for application output.	        
   CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
   	        "Systolic"                  INTEGER,
   	        "Diastolic"                 INTEGER,
   	        "BloodPressureLevel"        varchar(20),
   	        "ANOMALY_SCORE"             DOUBLE,
   	        "ANOMALY_EXPLANATION"       varchar(512));
   
   -- Compute an anomaly score with explanation for each record in the input stream
   -- using RANDOM_CUT_FOREST_WITH_EXPLANATION
   CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
      INSERT INTO "TEMP_STREAM"
         SELECT STREAM "Systolic", "Diastolic", "BloodPressureLevel", ANOMALY_SCORE, ANOMALY_EXPLANATION 
         FROM TABLE(RANDOM_CUT_FOREST_WITH_EXPLANATION(
                 CURSOR(SELECT STREAM * FROM "SOURCE_SQL_STREAM_001"), 100, 256, 100000, 1, true));
   
   -- Sort records by descending anomaly score, insert into output stream
   CREATE OR REPLACE PUMP "OUTPUT_PUMP" AS 
      INSERT INTO "DESTINATION_SQL_STREAM"
         SELECT STREAM * FROM "TEMP_STREAM"
         ORDER BY FLOOR("TEMP_STREAM".ROWTIME TO SECOND), ANOMALY_SCORE DESC;
   ```

**Next Step**  
[Step 3: Examine the Results](examine-results-with-exp.md)