Step 1: Prepare the Data - Amazon Kinesis Data Analytics for SQL Applications Developer Guide
Step 1.1: Create a Kinesis Data StreamStep 1.2: Write Sample Records to the Input Stream

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

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

2. 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.

Step 1: Prepare the Data

Before you create an Amazon Kinesis Data Analytics application for this example, you create a Kinesis data stream to use as the streaming source for your application. You also run Python code to write simulated blood pressure data to the stream.

Step 1.1: Create a Kinesis Data Stream

In this section, you create a Kinesis data stream named ExampleInputStream. You can create this data stream 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 https://console.aws.amazon.com/kinesis.

    2. Choose Data Streams in the navigation pane. Then choose Create Kinesis stream.

    3. For the name, type ExampleInputStream. For the number of shards, type 1.

  • Alternatively, to use the AWS CLI to create the data stream, run the following command:

    $ aws kinesis create-stream --stream-name ExampleInputStream --shard-count 1

Step 1.2: Write Sample Records to the Input Stream

In this step, you run Python code to continuously generate sample records and write them to the data stream that you created.

  1. Install Python and pip.

    For information about installing Python, see Python.

    You can install dependencies using pip. For information about installing pip, see Installation in the pip documentation.

  2. Run the following Python code. You can change the Region to the one you want to use for this example. 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 PressureType(Enum):
    low = "LOW"
    normal = "NORMAL"
    high = "HIGH"


def get_blood_pressure(pressure_type):
    pressure = {"BloodPressureLevel": pressure_type.value}
    if pressure_type == PressureType.low:
        pressure["Systolic"] = random.randint(50, 80)
        pressure["Diastolic"] = random.randint(30, 50)
    elif pressure_type == PressureType.normal:
        pressure["Systolic"] = random.randint(90, 120)
        pressure["Diastolic"] = random.randint(60, 80)
    elif pressure_type == PressureType.high:
        pressure["Systolic"] = random.randint(130, 200)
        pressure["Diastolic"] = random.randint(90, 150)
    else:
        raise TypeError
    return pressure


def generate(stream_name, kinesis_client):
    while True:
        rnd = random.random()
        pressure_type = (
            PressureType.low
            if rnd < 0.005
            else PressureType.high
            if rnd > 0.995
            else PressureType.normal
        )
        blood_pressure = get_blood_pressure(pressure_type)
        print(blood_pressure)
        kinesis_client.put_record(
            StreamName=stream_name,
            Data=json.dumps(blood_pressure),
            PartitionKey="partitionkey",
        )


if __name__ == "__main__":
    generate(STREAM_NAME, boto3.client("kinesis"))
Next Step

Step 2: Create an Analytics Application