Amazon Kinesis Data Analytics
Developer Guide

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 will use 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.

    import json from boto import kinesis import random kinesis = kinesis.connect_to_region("us-east-1") # generate normal heart rate with probability .99 def getNormalHeartRate(): data = {} data['heartRate'] = random.randint(60, 100) data['rateType'] = "NORMAL" return data # generate high heart rate with probability .01 (very few) def getHighHeartRate(): data = {} data['heartRate'] = random.randint(150, 200) data['rateType'] = "HIGH" return data while True: rnd = random.random() if (rnd < 0.01): data = json.dumps(getHighHeartRate()) print data kinesis.put_record("ExampleInputStream", data, "partitionkey") else: data = json.dumps(getNormalHeartRate()) print data kinesis.put_record("ExampleInputStream", data, "partitionkey")

Next Step

Step 2: Create an Application