AWS Glue: Complete ETL Guide with PySpark Examples

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. Unlike traditional ETL tools that require infrastructure management, Glue handles the heavy lifting—you focus on your data transformations.

The performance difference is dramatic. Without pushdown predicates, a job reading a year’s worth of partitioned data might scan 365 partitions. With the predicate above, it reads only one. I’ve seen job runtimes drop from 45 minutes to under 3 minutes just by adding appropriate pushdown predicates.

Glue Workflows: Orchestrating ETL Pipelines

For pipelines with multiple dependent jobs, Glue Workflows provide built-in orchestration. A workflow chains crawlers, jobs, and triggers together with dependency management.

That said, for complex orchestration requirements, I often recommend Step Functions over Glue Workflows. Step Functions provides more flexible error handling, branching logic, and integration with non-Glue services. Glue Workflows are simpler but less powerful — they work well for linear pipelines but struggle with complex DAG patterns.

Cost Optimization

• Use Auto Scaling: Let Glue automatically adjust the number of workers based on workload characteristics rather than over-provisioning for peak
• Optimize DPU Hours: More workers don’t always mean faster jobs due to diminishing returns and shuffle overhead
• Enable Flex Execution: Lower-cost option for non-urgent workloads that can tolerate potential delays in execution start
• Monitor with CloudWatch: Track DPU utilization metrics to right-size workers and detect waste

Glue vs. Alternatives

Choosing between Glue, EMR, and Athena depends on your specific use case. Here’s how I think about the decision:

My rule of thumb: use Glue for standard batch ETL workloads, EMR when you need full control over your Spark/Hadoop cluster, and Athena for ad-hoc queries and light transformations. Most organizations end up using all three in combination — Glue for data preparation, Athena for exploration, and EMR for specialized processing.

Further Reading