Overview
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that simplifies data preparation for analytics. This guide details the steps to extract data from two DynamoDB tables, transform it using AWS Glue, load it into Amazon S3, and analyze it using Amazon Athena.

Why Use AWS Glue?
Serverless Architecture: AWS Glue eliminates the need for server management, allowing users to focus on data integration without worrying about underlying infrastructure. This serverless model ensures that resources scale automatically based on workload.

Automated Data Cataloging: AWS Glue’s Data Catalog automatically discovers and stores metadata about data sources, making it easy to manage and access data. The catalog can integrate with various AWS services, providing a unified view of your data landscape.

Seamless Integration: AWS Glue natively integrates with a range of AWS services, such as DynamoDB, S3, and Athena, simplifying the process of moving data across the AWS ecosystem.

Support for Various Data Sources: AWS Glue supports multiple data formats and sources, making it versatile for different use cases. This flexibility allows organizations to centralize their data preparation efforts.

Scenario: Integrating Data from DynamoDB to S3 and Querying with Athena

Step 1

Extracting Data from DynamoDB
To set up AWS Glue for extracting data from DynamoDB, refer to the AWS Glue documentation on creating crawlers. Crawlers will automatically scan your DynamoDB tables to populate the Data Catalog with metadata.

Create Crawlers
Navigate to the AWS Glue Console.
Create a new crawler to scan the Customers and Transactions tables in DynamoDB.

Step 2

Transforming Data with ETL Jobs
Once the Data Catalog is populated, you can create an ETL job to transform the data. The AWS Glue documentation on ETL jobs provides a comprehensive guide.

AWS Glue provides a variety of transformation types to help you prepare and process your data efficiently during the ETL process. For more, check AWS documentation about transforming data with AWS Glue managed transforms.

Define ETL Logic:

Use AWS Glue Studio to create a job that joins the Customers and Transactions tables. Here’s an example snippet:

joined_df = Join.apply(customers_df, transactions_df, 'CustomerID', 'CustomerID')

Load Transformed Data to S3:

Specify an S3 bucket as the output location. AWS Glue can store the data in various formats (e.g., Parquet, CSV), which enhances query performance in Athena.

View in AWS Glue:

Step 3

Querying Data with Amazon Athena
After loading the transformed data into S3, you can use Amazon Athena to query it. Follow the Athena documentation to set up a table that points to your S3 bucket.

Run SQL Queries:
Leverage the power of SQL to analyze your data. For instance:

SELECT Name, SUM(Amount) as TotalSpent
FROM ecommerce_data
GROUP BY Name
ORDER BY TotalSpent DESC;

And there you go—you now have your transformed data queried in Amazon Athena!