| Feature | AWS Glue | Amazon EMR |
|---|---|---|
| Description | A fully managed ETL service for data preparation, transformation, and cataloging. | A fully managed big data processing platform for running Hadoop, Spark, and other distributed frameworks. |
| Primary Use Case | ETL, data preparation, data cataloging. | Big data processing, analytics, and real-time stream processing. |
| Technology Stack | Built on Apache Spark for distributed ETL. | Supports a variety of frameworks: Hadoop, Spark, Hive, Presto, HBase, Flink, etc. |
| Ease of Use | No infrastructure to manage; drag-and-drop UI (Glue Studio/DataBrew) available for ETL workflows. | Requires more configuration and setup for managing clusters and frameworks. |
| Key Features | – Data Catalog. – Automatic schema discovery (Crawlers). – Visual ETL (Glue Studio). – Data preparation (DataBrew). – Serverless. | – Wide range of tools/frameworks. – Customizable clusters. – Real-time and batch processing. |
| Serverless | Yes, serverless and fully managed. | No, requires EC2 instances to create clusters, but supports autoscaling. |
| Data Integration | Works natively with S3, RDS, Redshift, DynamoDB, Athena, etc. | Works with S3, RDS, Redshift, and third-party data sources; ideal for Hadoop-based workloads. |
| Scalability | Automatically scales resources based on workload. | Manual or automated scaling via cluster configurations. |
| Performance | Optimized for ETL tasks and transformations. | Suitable for a wide variety of workloads, including batch processing, machine learning, and streaming analytics. |
| Cost Model | Pay-per-use: Charged based on DPUs (Data Processing Units) and runtime. | Pay-per-use: Charged based on EC2 instances, cluster size, and duration. |
| Supported Languages | Python (PySpark) or Scala for ETL scripts. | Supports Java, Python, Scala, and SQL (via Presto, Hive, etc.). |
| Best For | – ETL pipelines. – Building and maintaining data lakes. – Data cataloging and schema discovery. | – Big data processing. – Machine learning model training. – Streaming and batch analytics. |
| When to Use | – Need a serverless, low-maintenance ETL solution. – Simplify data preparation and transformations. | – Running diverse big data workloads. – Require a specific tool like Hive, HBase, Flink, or Presto. |