The full answer
Use EMR when you need control, Glue when you want to run Spark with the least to manage. EMR suits frameworks other than Spark, custom libraries and tuning, steady heavy workloads where cheaper EC2 capacity matters, and long-running clusters; Glue suits scheduled ETL jobs, catalog-centred pipelines and teams that don't want to tune clusters.
| AWS Glue | Amazon EMR | |
|---|---|---|
| Model | Serverless jobs | Clusters on EC2 or EKS, or EMR Serverless |
| Engines | Spark (and Python shell) | Spark, Hive, Trino, Presto, Flink, HBase and more |
| Control | Worker type and count, Spark settings | Instance types, Spot, configuration, bootstrap actions, versions |
| Start-up | Seconds to a minute or two | Minutes for a new cluster; EMR Serverless is quicker |
| Extras | Data Catalog, crawlers, visual Glue Studio, Data Quality, bookmarks, connectors | Full ecosystem, notebooks in EMR Studio, YARN or Kubernetes |
| Pricing | Per DPU-hour while a job runs | EC2 (Spot or Reserved) plus an EMR charge, or per use on Serverless |
Choose EMR when
- you need engines other than Spark (Flink, Trino, HBase, Hive) or specific open-source versions;
- you need custom native libraries, GPU instances or deep tuning of the cluster;
- workloads are large and steady, where Spot instances and right-sized clusters cost much less than Glue's per-DPU price;
- you run interactive or long-running clusters, for example for data science.
Choose Glue when
- the job is ETL between S3, databases and the warehouse, run on a schedule or trigger;
- you want the Data Catalog, crawlers, job bookmarks, Data Quality and visual authoring built in;
- the team should not manage clusters at all;
- workloads are spiky or small, where paying only for run time wins.
EMR Serverless sits between them: EMR's open-source runtimes without clusters to manage, billed for resources used, and a common choice for Spark jobs that outgrow Glue's options.
A good answer avoids "EMR for real-time, Glue for batch" as the main rule (Glue has streaming jobs too) and focuses on control, engines and cost at scale.
Last updated 16 October 2026
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