Understanding AWS Glue

AWS Glue has gotten complicated with all the components, job types, and integration patterns flying around. As someone who has built ETL pipelines with Glue across multiple production environments, I learned everything there is to know about this serverless data integration service. Today, I will share it all with you.

Here’s my honest take: Glue isn’t the sexiest AWS service, but it might be one of the most useful ones if you work with data. It quietly handles the grunt work that makes data analytics and machine learning possible — extracting data from sources, transforming it into something useful, and loading it into destinations where it can be queried and analyzed.

AWS Glue Catalog

Probably should have led with this section, honestly. The Glue Data Catalog is the foundation that everything else builds on. Think of it as a centralized metadata repository — it stores table definitions, schema information, and metadata about your data assets. Both Athena and Redshift Spectrum query against the Catalog, so any table you define here becomes queryable across multiple services without duplication.

I use the Data Catalog as the single source of truth for all data schemas in my environments. When someone asks “where does this data live and what does it look like?”, the answer is always “check the Catalog.” That consistency is invaluable when you have dozens of data sources and multiple teams querying them.

The Catalog is also Hive Metastore compatible, which means if you’re coming from a Hadoop background, you can point your existing tools at it without major changes. Nice touch by the Glue team.

Data Crawlers

Crawlers are Glue’s automated schema discovery mechanism. You point a crawler at a data source — an S3 bucket, a JDBC database, a DynamoDB table — and it scans the data, infers the schema, and creates or updates table definitions in the Data Catalog.

I run crawlers on a schedule (usually nightly) for data sources that change regularly. When new partitions appear in S3, the crawler picks them up automatically. When schema changes happen, the crawler detects them and updates the Catalog. It’s not perfect — I’ve had crawlers get confused by mixed file formats in the same prefix — but for the vast majority of use cases, it works remarkably well.

Pro tip: set up separate crawlers for separate data sources rather than one massive crawler that scans everything. Isolated crawlers are easier to debug when something goes wrong, and they run faster because they’re scanning less data.

ETL Jobs

That’s what makes Glue endearing to us data engineers — the ETL jobs run on a fully managed Apache Spark infrastructure that you never have to think about. No cluster sizing, no Spark tuning, no managing YARN. You write your transformation logic, Glue handles the compute.

Glue ETL jobs can be written in Python (using PySpark) or Scala. I write almost everything in Python because the team maintains it more easily, but Scala jobs tend to perform better for complex transformations at scale. You choose based on your team’s skills and performance requirements.

Job bookmarks are a feature I rely on heavily. They track which data has already been processed, so subsequent job runs only process new data. Without bookmarks, your job would reprocess the entire dataset every time, which wastes compute and can cause duplicate records in your destination. Enable bookmarks on every job that processes incrementally arriving data.

Glue also offers several job types: Spark ETL (full Apache Spark), Spark Streaming (near-real-time), Python Shell (for lightweight scripts), and Ray (for distributed Python). Most of my work uses Spark ETL, but Python Shell jobs are great for simple transformations that don’t need the overhead of a Spark cluster.

Triggers and Workflows

Triggers let you automate when jobs run. You can trigger jobs on a schedule (cron-style), on demand, or based on events like another job completing. Workflows chain multiple crawlers and jobs together into a multi-step pipeline.

I build workflows for common data pipeline patterns: crawler discovers new data, first job cleans and validates it, second job transforms and enriches it, third job loads it into Redshift. The workflow handles the sequencing and error handling, so I don’t need an external orchestration tool for straightforward pipelines.

For more complex orchestration — conditional logic, human approvals, fan-out patterns — I tend to use Step Functions instead of Glue Workflows. But for linear data pipeline orchestration, Glue Workflows work great and keep everything within one service.

Development Endpoints

Development endpoints let you interactively develop and test Glue ETL scripts using Jupyter notebooks or a Zeppelin notebook. You connect to a running Spark environment where you can write code, test it against sample data, and iterate without running full jobs.

Fair warning: development endpoints aren’t cheap. They spin up dedicated compute resources that run until you stop them. I’ve been bitten by forgetting to shut down a dev endpoint over a long weekend. Now I set calendar reminders. For budget-conscious teams, Glue Studio’s visual editor or the local development Docker container are cheaper alternatives for testing.

Glue Studio

Glue Studio is the visual ETL tool that lets you build transformation pipelines by dragging and dropping nodes. It generates PySpark code under the hood, which you can view and customize. I’ve found it genuinely useful for two things: rapid prototyping of new pipelines and enabling non-engineering team members to build simple transformations on their own.

For production pipelines, I usually start in Glue Studio to lay out the basic flow, then export the generated code and customize it in my IDE. That combination gives me the speed of visual development with the control of hand-written code.

Best Practices

After building dozens of Glue pipelines, here are the practices I follow consistently:

• Always enable job bookmarks for incremental processing
• Use Parquet or ORC output formats for downstream query efficiency
• Partition your output data by date or other relevant dimensions
• Monitor job execution with CloudWatch metrics and set up alerts for failures
• Use Glue connections for JDBC sources and store credentials in Secrets Manager
• Test with small datasets first before running against production data volumes
• Version control your ETL scripts in Git, not just in the Glue console

Conclusion

AWS Glue does the hard work of data integration so you can focus on delivering insights. It’s not flashy, but it’s reliable and it scales. Whether you’re building your first ETL pipeline or managing complex data workflows across multiple accounts, Glue has the components to handle it. Just remember to clean up your dev endpoints when you’re done testing. Your finance team will appreciate it.