Both run big data / Spark-style workloads on AWS, but they sit at different points on the managed-vs-control spectrum.
AWS Glue is a serverless data integration service. You write ETL jobs (often PySpark), and Glue provisions the compute. You also get a Data Catalog (Hive-style metastore), crawlers, and Glue Studio. Pay for DPU-hours while the job runs.
Amazon EMR (Elastic MapReduce) is a managed cluster service for Hadoop/Spark/Hive/Presto/etc. You choose instance types, cluster size, and often keep clusters warm for interactive or heavy workloads. More knobs, more ops.
Glue (serverless job): submit PySpark ETL -> Glue runs -> dies when done + Data Catalog tables EMR (cluster): start cluster (master + workers) -> Spark/Hive/notebooks -> scale up/down or terminate when idle
Comparison
| Dimension | Glue | EMR | |---|---|---| | Model | Serverless jobs | Managed clusters | | Best for | Scheduled ETL, catalog-centric pipelines | Heavy Spark, custom libs, long sessions | | Ops burden | Low | Medium (AMI, bootstrap, autoscaling) | | Cost shape | Pay per job DPU time | Pay for EC2/EMR while cluster lives | | Flexibility | Constrained runtime | Full control of Spark/Hadoop stack |
Tiny scenario
- Nightly CSV → Parquet cleanse with few dependencies → Glue
- Large feature-engineering Spark job needing custom JARs and Jupyter → EMR (or EMR Serverless if you want less cluster babysitting)
Interview tip: "Glue = serverless ETL + catalog; EMR = managed Spark/Hadoop cluster." Pick Glue for simple scheduled jobs, EMR when you need cluster control or heavier interactive Spark.