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Using AWS Lambda With Amazon Kinesis Data Stream

In this lab, you will step into the shoes of a data engineer who is challenged with moving some taxi data from a Cloud9 instance to a DynamoDB table so the data analyst team can easily access and view it. To accomplish this, you will launch a CloudFormation stack, create a Kinesis Data Stream, create a DynamoDB table, format a Lambda function, and finally, move data from the Cloud9 instance to the DynamoDB table.

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Path Info

Level
Clock icon Beginner
Duration
Clock icon 1h 0m
Last updated
Clock icon Sep 01, 2025

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Table of Contents

  1. Challenge

    Create an Amazon Kinesis Data Stream

    Create a Kinesis Data Stream named input-stream.

  2. Challenge

    Populate Data on the Cloud9 Instance

    1. Add the lab1.py file from the lab GitHub repo to the Cloud9 instance.
    2. Install boto3 on the instance using pip install boto3.
    3. Run the newly created lab.py file with python lab.py.

  3. Challenge

    Create a DynamoDB Table

    Create a new DynamoDB table named kinesisAggs and add a partition key named vendorId with type of Number.

  4. Challenge

    Create and Configure a Lambda Function

    1. Create a new Lambda function named kinesisLambdaConsumer01.
    2. Set the IAM role to KinesisLambdaConsumerRole-Kinesislab
    3. Download and modify the lambda-deploy.zip file from the lab GitHub repo.

  5. Challenge

    Confirm Data is Populating to DynamoDB

    In the DynamoDB section of the console, explore items in the table and confirm that you have at least two entries.

    (If you are not seeing data after 5 minutes, your python script may have timed out. If you do not see data flowing in the CLI, rerun the python lab.py command and data should start flowing through in about 3-5 minutes)

  6. Challenge

    Create a CloudFormation Stack

    Create a CloudFormation stack using the template file provided in the GitHub repo for this lab named CFN Build.yaml.

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