How to Perform ETL Using AWS Glue: A Step-by-Step Guide

3 min readDec 4, 2024

In the ever-evolving world of data, ETL (Extract, Transform, Load) stands as a cornerstone for building efficient data pipelines. With AWS Glue, Amazon’s fully managed, serverless ETL service, this process becomes easier and more scalable than ever. Whether you’re handling large-scale data transformations or small batch jobs, AWS Glue offers a comprehensive suite of tools to simplify the ETL workflow.

This guide dives into the essentials of performing ETL using AWS Glue, from setup to execution.

What is ETL?

ETL represents the Extract, Transform, Load process:

Extract: Retrieving data from various sources.
Transform: Applying operations like filtering, cleaning, joining, or aggregating to make data usable.
Load: Storing the transformed data into a target destination, such as a data warehouse or data lake.

ETL is critical for ensuring that raw data from diverse sources is ready for analysis, reporting, or downstream processing.

Why Choose AWS Glue for ETL?

AWS Glue streamlines the ETL process with features designed to handle data workloads of any scale. Here’s what makes it stand out:

Fully Managed: Say goodbye to managing servers, scaling, or infrastructure. AWS Glue takes care of it all.
Serverless: Focus on your data and transformations without worrying about deployments.
Powered by Apache Spark: Built-in support for Apache Spark ensures fast and reliable data processing.
Multiple Development Options: Use visual tools, interactive notebooks, or custom PySpark scripts to build your ETL workflows.
Seamless Integration: Effortlessly connect to AWS services like S3, Redshift, and RDS, as well as external data sources.
Built-in Scheduling and Monitoring: Automate job runs with scheduling and track progress with real-time monitoring tools.

Step-by-Step: Creating Your First AWS Glue ETL Job

Let’s walk through a practical example of creating an ETL job using AWS Glue.

1. Prerequisites

Before starting, ensure you have:

Data in an S3 bucket: This serves as your source and/or target location.
IAM Role: A role with permissions to access S3 and Glue resources.

2. Creating an ETL Job

Here’s how you can set up your job using AWS Glue’s visual interface:

Step 1: Navigate to AWS Glue

Log into the AWS Console.
Access the Glue service and select “ETL Jobs.”

Step 2: Configure the Job Source

Choose Amazon S3 as the source type.
Specify the location of your S3 bucket containing the data.
Define the data format (e.g., CSV) and delimiter.
Optionally, preview the data and create a Glue catalog table to simplify source management.

Step 3: Add Transformations

Use the drag-and-drop interface to select transformations.
For this example, apply the Drop Fields transformation to remove the “last name” column.

Step 4: Configure the Target

Set the target type to Amazon S3.
Specify the output bucket location, format (e.g., Parquet), and compression type.
Link the target to the transformation step.

Step 5: Save the Job

Assign a name to your ETL job (e.g., “customers_drop_last_name”).
Review the configuration and save the job.

3. Running the ETL Job

Once your job is saved, you can run it directly from the Glue console.
AWS Glue automatically generates an Apache Spark-based script to execute the ETL steps:
Reads the data into a Glue Dynamic Frame.
Applies transformations like dropping fields.
Writes the processed data back to the target S3 bucket.

Monitor the Job

Use the Glue console to monitor logs and outputs in real time.
AWS Glue manages the infrastructure, ensuring a smooth and error-free execution.

4. Review the Output

After the job completes successfully:

Navigate to the specified target S3 location.
Verify the transformed data, ensuring it meets your expectations.

Next Steps: Explore Advanced Features

This example covers the basics of AWS Glue’s ETL capabilities. For more complex use cases, AWS Glue supports custom PySpark scripts, enabling you to implement sophisticated transformations and logic tailored to your data needs.

Conclusion

AWS Glue simplifies ETL, making it accessible to users with varying levels of technical expertise. Whether you’re a seasoned data engineer or new to data pipelines, Glue’s powerful features, seamless integrations, and serverless infrastructure make it an excellent choice for modern ETL workflows.

Start your AWS Glue journey today and unlock the potential of your data!