Mastering Data Ingestion and Analysis with AWS: A Hands-On Guide

In today’s era of data-driven decision-making, having the ability to efficiently extract insights from your data is essential for staying competitive. AWS provides a range of powerful tools for data ingestion and analysis, and in this guide, we’ll walk you through three practical use cases, complete with hands-on steps, to showcase the capabilities of AWS services.

Use Case 1: Batch Data Ingestion with AWS Data Pipeline

In this scenario, let’s consider a situation where you need to ingest data from your SQL database into a data lake for predictive analytics and performance measurement. We’ll use AWS Data Pipeline for this purpose.

  1. Accessing AWS Data Pipeline:
    • Log in to your AWS Management Console.
    • Navigate to the AWS Data Pipeline service.
  2. Creating a Data Pipeline:
    • Click on “Get Started Now” to create your first data pipeline.
    • Provide a name for your pipeline.
  3. Configuring the Source:
    • Choose a source. For this example, we’ll use an existing AWS managed template.
    • Configure the source to take a full copy of your RDS MySQL table and export it to Amazon S3.
  4. Metadata Configuration:
    • Provide the necessary metadata for the database, including MySQL password, S3 output folder, database username, and the table name.
  5. Pipeline Activation:
    • Choose the appropriate settings for pipeline activation. For this demo, we’ll run the pipeline only on activation.
    • Configure logging and security options. For this purpose, disable logging and use the default service role.
  6. Activating the Pipeline:
    • Click “Activate” to start the data pipeline.
    • Accept any validation warnings and activate the pipeline.
  7. Monitor Execution:
    • After activation, refresh the page to monitor the progress of the task runner initializing and executing the pipeline.

Use Case 2: Real-time Data Ingestion with Kinesis Firehose

In this use case, we’ll demonstrate how to ingest real-time data from IoT devices into your data lake using AWS Kinesis Firehose.

  1. Creating a Kinesis Firehose Delivery Stream:
    • Navigate to AWS Kinesis in the AWS Management Console.
    • Select “Kinesis Data Firehose” and click “Create Delivery Stream.”
  2. Configuring the Delivery Stream:
    • Provide a name for the delivery stream.
    • Choose the default option for adding objects directly to the stream.
  3. Optional Transformations:
    • You can transform source records using AWS Lambda or adjust the record format. For this demo, we’ll leave these options as default.
  4. Choosing a Destination:
    • Select Amazon S3 as the destination for your data.
    • You can optimize caching, compression, encryption, and other settings on this screen.
  5. Optimize and Finalize:
    • Optimize the settings according to your requirements.
    • Review the setup and click “Create Delivery Stream.”
  6. Ingesting Data:
    • Use the AWS SDK or command line interface to put records directly onto Amazon S3 using your Kinesis Firehose.

Use Case 3: Automated Data Exchange with AWS Data Exchange Subscriber Coordinator

For this scenario, let’s explore automating the download of third-party weather forecasting data from AWS Data Exchange to Amazon S3.

  1. Deploying the Subscriber Coordinator:
    • Access the AWS Data Exchange Subscriber Coordinator implementation guide on the AWS website.
    • Launch the coordinator in the AWS console.
  2. Regional Configuration:
    • Set your region to the desired location. For this example, switch to the Oregon region.
  3. Configuring the Stack:
    • Provide a stack name, such as “Automate Data Exchange.”
    • Choose the product you want to use and provide the dataset ID.
    • Configure Lambda ID for notifications, logging level, destination bucket, and subscription prefix.
  4. Review and Create:
    • Review the parameters you’ve provided.
    • Set notifications for IAM and privileges creation.
    • Click “Create Stack” to automate data set downloads.


By following these hands-on steps, you’ve gained practical experience in utilizing AWS services for efficient data ingestion and analysis. Whether you’re dealing with batch data processing, real-time ingestion, or third-party data exchange, AWS provides the tools to streamline your data workflows and empower your decision-making processes. As you continue to explore AWS services, you’ll uncover even more ways to harness the power of your data for business success.

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