AWS Glue for ETL (Extract, Transform, Load) + S3, RDS and Redshift [FULL TUTORIAL]

Introduction

This tutorial provides a step-by-step guide on using AWS Glue for ETL (Extract, Transform, Load) processes. AWS Glue is a powerful data integration service that helps to prepare and load data for analytics. In this guide, we will cover how to use AWS Glue alongside Amazon S3, Amazon RDS, and Amazon Redshift to create an organized data warehouse.

Step 1: Getting Started with AWS Glue

• Sign into AWS Management Console: Access the AWS Glue service through the console.
• Create a Glue Role: Ensure you have an IAM role with appropriate permissions for access to S3, RDS, and Redshift.
• Set up Glue: Navigate to AWS Glue and familiarize yourself with the interface and available features.

Step 2: Working with Amazon S3

Create an S3 Bucket

• Go to the S3 service in the AWS Management Console.
• Click on "Create Bucket" and follow the prompts to set it up.
• Upload Data: Add the data files you want to work with into your S3 bucket.

Step 3: Create a Database in AWS Glue

• Navigate to the Glue Console: Click on "Databases" on the left sidebar.

Create a New Database

• Click on “Add Database”.
• Provide a name and description for your new database.

Step 4: Add Tables Using Crawler

Create a Crawler

• Go to the "Crawlers" section in the Glue interface.
• Click on “Add Crawler” and follow the steps to set it up.

Configure the Crawler

• Specify your S3 bucket as the data source.
• Set the crawler to run and create tables based on the data structure.
• Run the Crawler: After configuration, run the crawler to populate your database with tables.

Step 5: Query the Data with Athena

Navigate to AWS Athena

• Select the Glue database you created.
• Run SQL Queries: Use Athena to run SQL queries on your data to validate that the tables have been created correctly.

Step 6: Transforming the Data

Change Schema

• Select the Table to Transform: Use the Glue interface to find the table you want to modify.

Edit Schema

• Adjust the column types as needed for your ETL process.

Join Two Data Sources

Use Glue Studio

• Access Glue Studio to create a new job for your transformation.
• Add Data Sources: Select both data sources you wish to join.
• Configure Join Conditions: Set up the join logic to merge the two datasets effectively.

Step 7: Working with Amazon RDS

Setup RDS Instance

• Go to RDS service and create a new database instance (e.g., MySQL).

Connect to RDS

• Use MySQL Workbench or a similar tool to connect to your RDS database.
• Ensure your security groups allow access from your IP address.

Step 8: Load Data into Amazon Redshift

Create a Redshift Cluster

• Navigate to the Redshift service and create a new cluster.

Load Data

• Use Glue jobs to load transformed data into Redshift tables.

Step 9: Clean Up

• Delete Resources: After completing your project, remember to clean up by deleting your S3 bucket, Glue database, RDS instance, and Redshift cluster to avoid ongoing charges.

Conclusion

In this tutorial, you learned how to use AWS Glue for ETL processes, including setting up data sources in S3 and RDS, creating a database, managing tables, and loading data into Redshift. This foundational knowledge allows you to integrate and analyze data effectively. For further learning, consider exploring more advanced features of AWS Glue and data analytics services.