Step 1: Prepare - Amazon Kinesis Data Analytics for SQL Applications Developer Guide

For new projects, we recommend that you use the new Managed Service for Apache Flink Studio over Kinesis Data Analytics for SQL Applications. Managed Service for Apache Flink Studio combines ease of use with advanced analytical capabilities, enabling you to build sophisticated stream processing applications in minutes.

Step 1: Prepare

Before you create an Amazon Kinesis Data Analytics application for this exercise, you must create two Kinesis data streams. Configure one of the streams as the streaming source for your application, and the other stream as the destination where Kinesis Data Analytics persists your application output.

Step 1.1: Create the Input and Output Data Streams

In this section, you create two Kinesis streams: ExampleInputStream and ExampleOutputStream. You can create these streams using the AWS Management Console or the AWS CLI.

  • To use the console
    1. Sign in to the AWS Management Console and open the Kinesis console at

    2. Choose Create data stream. Create a stream with one shard named ExampleInputStream. For more information, see Create a Stream in the Amazon Kinesis Data Streams Developer Guide.

    3. Repeat the previous step, creating a stream with one shard named ExampleOutputStream.

  • To use the AWS CLI
    1. Use the following Kinesis create-stream AWS CLI command to create the first stream (ExampleInputStream).

      $ aws kinesis create-stream \ --stream-name ExampleInputStream \ --shard-count 1 \ --region us-east-1 \ --profile adminuser
    2. Run the same command, changing the stream name to ExampleOutputStream. This command creates the second stream that the application uses to write output.

Step 1.2: Write Sample Records to the Input Stream

In this step, you run Python code to continuously generate sample records and write these records to the ExampleInputStream stream.

{"heartRate": 60, "rateType":"NORMAL"} ... {"heartRate": 180, "rateType":"HIGH"}
  1. Install Python and pip.

    For information about installing Python, see the Python website.

    You can install dependencies using pip. For information about installing pip, see Installation on the pip website.

  2. Run the following Python code. The put-record command in the code writes the JSON records to the stream.

    from enum import Enum import json import random import boto3 STREAM_NAME = "ExampleInputStream" class RateType(Enum): normal = "NORMAL" high = "HIGH" def get_heart_rate(rate_type): if rate_type == RateType.normal: rate = random.randint(60, 100) elif rate_type == RateType.high: rate = random.randint(150, 200) else: raise TypeError return {"heartRate": rate, "rateType": rate_type.value} def generate(stream_name, kinesis_client, output=True): while True: rnd = random.random() rate_type = RateType.high if rnd < 0.01 else RateType.normal heart_rate = get_heart_rate(rate_type) if output: print(heart_rate) kinesis_client.put_record( StreamName=stream_name, Data=json.dumps(heart_rate), PartitionKey="partitionkey", ) if __name__ == "__main__": generate(STREAM_NAME, boto3.client("kinesis"))

Next Step

Step 2: Create an Application