AWS Glue is a managed ETL (Extract, Transform, Load) service for moving data between AWS products such as S3, RDS, and Redshift. I tested it out for moving S3 data into Redshift, and transforming JSON data to CSV format in S3.

How It Works

Although the split concepts of Crawlers, Databases, Tables, Jobs, and Triggers is confusing at first, the product is fairly easy to use after enduring the initial learning curve. Here’s how I would break it down.

Crawlers look at your data source and try to understand the schema of the data it contains. It stores that schema as a…

Table which is part of a Database. A Table is essentially a virtual object definition, created by a Crawler. It can be the schema of CSV’s in S3 (and many other data formats in S3), or an actual database table from Redshift, RDS, or any database that is accessible via JDBC. Once you have at least one Table defined, you can move the data using a Job.

A Job is a task that copies data from one place to another. You can move S3 data to Redshift, copy a Redshift table to S3, copy an RDS table to Redshift…the sky’s the limit.

A Trigger is a configuration for when to run a Job or set of Jobs. This can be on a schedule, such as daily, or fired on Job Events, such as when a Job succeeds or fails.

How It Performed In My Limited Testing

I set up several Crawlers, Tables, and Jobs to test out Glue. One was simply taking a set of JSON files in S3 (approximately 90,000 files of under 1MB each) and flattening them to CSV files, storing the new files in S3 as well. All told, this is about 7GB of total data to be transformed. (more…)