AWS Glue is a fully managed serverless data integration service that eliminates the complexity of building and managing data infrastructure. When combined with Terraform, which allows developers and operations teams to define, provision, and manage cloud infrastructure using a declarative language, the result is a repeatable, version-controlled data platform. Terraform’s powerful state management and planning capabilities enable teams to collaborate efficiently and maintain consistent infrastructure across different environments.
Using Terraform to deploy AWS Glue Data Quality pipeline enables Infrastructure as Code best practices to ensure consistent, version controlled and repeatable deployments across multiple environments, while fostering collaboration and reducing errors due to manual configuration. This article covers how to build ETL pipelines, enforce data quality, and manage Glue resources with Terraform modules and community patterns.
What AWS Glue Is and Why Terraform Matters
AWS Glue is a fully managed, serverless data integration service that eliminates the complexity of building and managing data infrastructure. It provides Glue Jobs for processing files, Glue Crawler for cataloging the data, and integrates with the Glue Data Catalog for metadata management. Glue Jobs run Apache Spark under the hood and can process data in S3, with results written back to S3 or other destinations.
Terraform brings Infrastructure as Code discipline to Glue. With Terraform, you can version, share, and reuse your infrastructure code across multiple cloud providers and services. Its powerful state management and planning capabilities enable teams to collaborate efficiently and maintain consistent infrastructure across different environments.
By leveraging Terraform, you can automate AWS Glue ETL pipeline, reduce manual errors, and maintain consistent configurations across environments. This is especially valuable for teams managing schema changes, securing ETL processes with IAM roles, or monitoring jobs with CloudWatch.
AWS Glue Data Quality with Terraform
AWS Glue Data Quality is a feature of AWS Glue that helps maintain trust in your data and support better decision-making and analytics across your organization. It allows users to define, monitor, and enforce data quality rules across their data lakes and data pipelines.
It allows developers and operations teams to define, provision, and manage cloud infrastructure using a declarative language. With Terraform, you can version, share, and reuse your infrastructure code across multiple cloud providers and services.
With AWS Glue Data Quality, you can automatically detect anomalies, validate data against predefined rules, and generate quality scores for your datasets. This feature provides flexibility in how you validate your data – you can incorporate quality checks into your ETL processes for transformation-time validation, or validate data directly against cataloged tables for ongoing data lake monitoring. By leveraging machine learning, it can also suggest data quality rules based on your data patterns.
Two Complementary Data Quality Approaches
In practice, two complementary methods for implementing AWS Glue Data Quality using Terraform are used together for comprehensive coverage.
- ETL-based Data Quality – Validates data during ETL job execution, generating detailed quality metrics and row-level validation outputs
- Catalog-based Data Quality – Validates data directly against Glue Data Catalog tables without requiring ETL execution, ideal for monitoring data at rest
This post demonstrates how to implement AWS Glue Data Quality pipelines using Terraform using two complementary approaches mentioned above to ensure comprehensive data quality across your data lake.
A real-world public dataset such as the NYC yellow taxi trip data can be used to illustrate data quality validation and monitoring capabilities.
| Approach | Validation Timing | Execution Requirement | Typical Use Case |
|---|---|---|---|
| ETL-based Data Quality | During ETL job execution | Glue Job must run | Transformation-time validation with row-level outputs |
| Catalog-based Data Quality | Directly against cataloged tables | No ETL execution required | Ongoing data lake monitoring at rest |
Building an S3 to Glue ETL Pipeline with Terraform
Creating an ETL pipeline with Amazon Web Services AWS Glue and HashiCorp Terraform can significantly streamline your data processing tasks. The common pattern is to read data from an S3 bucket, process it with PySpark, and write the transformed data back to another S3 bucket.
Amazon S3 Simple Storage Service is an object storage service that provides scalability, data availability, security, and performance. It serves as the raw and processed data store for Glue jobs.
A typical Terraform-driven pipeline includes:
- Amazon S3: for storing our raw and processed data
- AWS Glue:
- Glue Job: For processing files
- Glue Crawler for cataloging the data
- Glue Data Catalog for metadata
By following this guide, you’ve learned how to read data from S3, transform it using PySpark, and write the results back to S3. We’ve covered best practices for AWS Glue ETL jobs, including configuring the Glue Data Catalog and setting up Glue crawlers.
With Terraform, you can easily manage and deploy these resources, making your ETL process more reliable and repeatable.
AWS Glue offers powerful tools for large data sets, and by integrating it with Terraform, you can optimize your workflows and enhance performance. Whether you’re handling schema changes, securing your ETL processes with IAM roles, or monitoring jobs with CloudWatch, AWS Glue and Terraform provide a comprehensive solution for your data transformation needs.
A minimal Terraform snippet for an S3 bucket and Glue job IAM role looks like:
```hcl
resource "awsiamrole" "glueservicerole" {
name = "glue-service-role"
assumerolepolicy = jsonencode({
Version = "2012-10-17"
Statement = [{
Effect = "Allow"
Principal = { Service = "glue.amazonaws.com" }
Action = "sts:AssumeRole"
}]
})
}
resource "awss3bucket" "raw" {
bucket = "glue-raw-data"
}
```
AWS Glue Jobs with Terraform in Practice
Using AWS Glue Jobs with Terraform is a common pattern for serverless Spark workloads. Discover how AWS Glue Jobs quietly binds your data journey with the agility of serverless Spark.
A practical service set for an end-to-end workflow includes:
- Amazon S3: for storing our raw and processed data
- AWS Glue:
- Glue Job: For processing files
- Glue Crawler for cataloging the data
- Step-function: For executing the whole workflow
- Lambda: For triggering the step function upon file upload in the S3 bucket
This is a Level 200 post; following along with the post and deploying the infrastructure to your AWS account will cost approximately ~$1.5 per month, given you run ~10 times a month, the Glue job and the Crawler. You can follow along with the code in my repo.
The workflow converts CSV files to Parquet, catalogs the data, and triggers on new S3 events. Terraform modules from Terraform AWS Modules are typically used to provision the majority of those resources.
Terraform Modules for Glue
Terraform modules for provisioning and managing AWS Glue resources simplify reuse.
The following Glue resources are supported:
- Glue catalog database
- Glue crawler
- Glue job
- Glue trigger
- IAM roles and policies
Tip
For a complete example, see examples/complete. The example provisions a Glue catalog database and a Glue crawler that crawls a public dataset in an S3 bucket and writes the metadata into the Glue catalog database. It also provisions an S3 bucket with a Glue Job Python script, and a destination S3 bucket for Glue job results. And finally, it provisions a Glue job pointing to the Python script in the S3 bucket, and a Glue trigger that triggers the Glue job on a schedule
This pattern is useful for teams wanting a production-ready module set with minimal configuration.
Terraglue Open Source Module
Terraglue is an open source Terraform module developed in order to provide an easy way to deploy a Glue job in any AWS account.
The module answers several common questions:
- Are you using Glue for the first time and want to see an end to end ETL example in AWS?
- Do you already have a Spark application and want to deploy it as a Glue job in AWS?
- Do you want to automate the Glue job setup using an IaC tool such as Terraform?
- Have you ever wanted to go the next level on developing Glue jobs?
When terraglue module is called in a Terraform project, an operation mode must be chosen. There are two options: "learning" mode and "production" mode.
The learning mode helps users to understand more about Glue jobs on AWS by providing a complete example with all resources needed to start exploring Glue.
Key capabilities advertised:
- Available in two different operation modes: "learning" and "production"
- Possibility to deploy a preconfigured Glue job with a complete end-to-end ETL example when using "learning" mode
- Possibility to deploy a custom Glue job according to user needs when using "production" mode
- Have your Glue job ready and running at the touch of a Terraform module call
Now the terraglue project has an official documentation in readthedocs! Visit the following link and check out usability technical details, practical examples and more!
Practical Architecture Patterns
A common serverless ETL architecture with Terraform includes:
- Landing S3 bucket for raw ingestion
- Glue Crawler to infer schema and populate Data Catalog
- Glue Job with PySpark script stored in S3 for transformation
- Processed S3 bucket for Parquet output
- Glue Data Quality rules attached to the job or catalog table
- EventBridge or Lambda to trigger Step Functions on S3 put events
- CloudWatch for monitoring and alerting
Terraform enables you to codify all of these resources, define dependencies, and apply them consistently across dev, staging, and production.
Best Practices and Cost Considerations
- Use IAM roles with least privilege for Glue service role and job role
- Store Glue job scripts in versioned S3 buckets and reference them by path
- Parameterize bucket names, job names, and IAM policies via Terraform variables
- Use Glue Data Catalog for central metadata instead of maintaining separate schemas
- Schedule crawlers and jobs based on data arrival patterns to avoid idle costs
- Enable job bookmarks for incremental processing
- Monitor job metrics in CloudWatch and set alarms for failures
- Test Data Quality rules in learning mode before enforcing in production
With this knowledge, you’re now equipped to tackle real-world ETL challenges and take full advantage of serverless data processing.
Conclusion
AWS Glue and Terraform together provide a powerful combination for building reliable, observable, and repeatable data pipelines. Terraform’s declarative model brings version control, planning, and collaboration to Glue infrastructure, while Glue provides serverless Spark ETL and built-in data quality capabilities.
The ETL-based Data Quality approach validates data during job execution and produces row-level outputs, whereas Catalog-based Data Quality enables continuous monitoring of data at rest without re-running jobs. Both approaches can be codified in Terraform for consistent deployment.
Community modules such as cloudposse/terraform-aws-glue and terraglue accelerate adoption by providing ready-to-use patterns for jobs, crawlers, triggers, and end-to-end examples. Using learning mode for onboarding and production mode for custom workloads helps teams progress from experimentation to operational pipelines with minimal manual configuration.
By defining S3 buckets, IAM roles, Glue jobs, crawlers, and Data Quality rules as code, organizations achieve consistent infrastructure across environments, reduce manual errors, and maintain an auditable history of changes. This is the foundation for scalable data lakes and trustworthy analytics on AWS.