AWS Glue vs Databricks on AWS — Which Should You Use?
Both AWS Glue and Databricks run Apache Spark on AWS. Both transform data. Both connect to S3. So why do some AWS shops use Glue and others use Databricks — and how do you know which one is right for a given situation?
What AWS Glue actually is
AWS Glue is a fully managed serverless ETL service. You write Python or Scala scripts using the Glue DynamicFrame API (or standard PySpark), and Glue provisions the Spark cluster, runs your job, and tears it down — you only pay for the compute time used.
Glue also includes a data catalog (schema registry), crawlers (automatic schema detection), and Studio (a visual ETL builder). It is tightly integrated with the AWS ecosystem — IAM, S3, Athena, Redshift, and Lake Formation all work natively with Glue.
What Databricks on AWS adds
Databricks is a managed Spark platform — but much more than a job runner. It adds interactive notebooks, MLflow for ML experiment tracking, Delta Live Tables for declarative pipeline management, Unity Catalog for data governance, and a collaborative workspace.
Databricks clusters are persistent and interactive — you can run ad-hoc queries, iterate on transformation logic, and share notebooks with colleagues. Glue jobs are batch-only — you submit a job, it runs, it stops.
When to use Glue
AWS-only stack with simple to medium complexity transformations. Teams that want serverless with no cluster management. Organizations already deeply in the AWS ecosystem where native IAM and Lake Formation integration matters.
Glue is excellent for: S3 to Redshift loads, data catalog management, schema crawling, and straightforward batch ETL that runs on a schedule.
Glue is not great for: complex iterative development, real-time debugging, ML pipelines, or teams that want a collaborative notebook environment.
When to use Databricks
Teams doing serious data engineering with complex transformations, ML workloads, or real-time streaming. Organizations that want the same platform to work across AWS, Azure, and GCP. Engineers who want a proper development environment with interactive notebooks and version control.
Cost consideration: Databricks costs more than Glue for equivalent compute. But developer productivity on complex jobs is significantly higher, which often offsets the cost.
Job market: Databricks knowledge transfers across clouds. Glue knowledge is AWS-specific.