Glue, EMR and Athena overlap on the surface. What each is good at, what each costs, and how to choose.
Glue, EMR and Athena all read data in S3, run SQL or Spark, and write transformed output, so teams often struggle to choose between them. The differences are in operational model, cost structure, and what you actually control.
AWS Glue: a managed ETL service built on Apache Spark (and Python shell for simple jobs). You write Spark code or use Glue's visual ETL designer, and AWS handles cluster provisioning, scaling, and tear-down. Deeply integrated with the Glue Data Catalog for schema management.
Amazon EMR: a managed cluster service that runs the full Hadoop ecosystem — Spark, Hive, HBase, Presto, Flink, and more. You have full control over the cluster: instance types, Spark configuration, bootstrap actions, and application versions. Operationally heavier than Glue.
Amazon Athena: a serverless SQL query engine. Athena does not ETL — it queries data in place on S3 using Presto under the hood. Use it for ad-hoc analysis, operational reporting, and querying Iceberg tables. Not a substitute for ETL pipelines.
Book a free 30-minute call. You'll leave knowing what we'd do first and roughly what it costs, whether or not you hire us.
Glue pricing is based on DPU-hours. One DPU is 4 vCPUs and 16GB memory. The minimum billing is 10 minutes, minimum 2 DPUs. A typical Glue job using G.1X workers (1 DPU = 4 vCPU, 16 GB) at 10 workers costs $0.44 × 10 × (runtime in hours). A 1-hour job at 10 workers costs $4.40.
Glue 4.0 introduced G.025X workers (0.25 DPU each, 1 vCPU, 4 GB memory) — Flex workers that use spare AWS capacity at ~50% discount but with potential 5–10 minute startup delays. Use Flex workers for non-urgent batch jobs. For simple Python ETL jobs that do not need Spark, Glue Python Shell (0.0625 DPU/run) is extremely cheap.
EMR on EC2 gives you full control at the cost of managing clusters: instance selection, scaling policies, bootstrap actions, Spark configuration tuning (executor memory, parallelism, shuffle partitions). This is the right choice for teams with complex Spark jobs, custom libraries, specific performance requirements, or existing Hadoop infrastructure.
Athena is increasingly used as a lightweight ETL layer for simple S3-to-S3 transformations via CTAS (Create Table As Select). A CTAS query reads from a source table and writes Parquet output to a destination S3 prefix in one SQL statement — no Glue job required. This is excellent for aggregations, format conversions, and simple transformations that do not require Spark.
Athena cost optimization is critical at scale. The three levers: use Parquet or ORC format (column pruning + compression can reduce scan costs by 10–20×), design partition schemes so queries filter on partition columns (eliminating irrelevant files before scanning), and enable query result reuse (Athena can return cached results for identical queries within a configurable window).
Once a month: what changed in tech that month and what it means for you, from cloud and AI to security and the tools you build with. Every item links to its source. No sales pitch, unsubscribe in one click.
Book a free 30-minute call about it. Bring your setup and your bill; you'll leave knowing what we'd do first and roughly what it costs, whether or not you hire us.
Or keep reading
Building a Modern Data Lakehouse on AWS: Architecture, Tools, and Tradeoffs
A data lakehouse combines the scale of a data lake with the query performance and governance of a warehouse. The AWS services we use to run one in production, and how they fit together.
Read
First-Party Analytics on AWS for a Few Dollars a Month: The Pipeline Behind This Site
A visitor id, a session id and a page view id; a collector on our own domain; Firehose into S3; Athena with saved queries. How we built analytics that ad blockers do not drop, nobody else holds, and that costs less than a coffee at our traffic, and the three bugs we found on the way.
Read
As of November 12, 2024. Provided as is, for general information, and accurate to the best of our knowledge on the date shown. Not legal, compliance, tax, financial or other professional advice. Prices, limits and vendor services change; check current vendor documentation before relying on anything here. Terms of use.