When working with big data on AWS, two commonly used services for data processing are Amazon EMR and AWS Glue. Although both support scalable data transformation and analytics, they differ significantly in architecture, control, use cases, and cost models. Choosing the right tool depends on your specific workload, performance needs, and operational preferences.

In this post, we compare EMR and Glue across six key dimensions to help you make an informed decision.

1. Execution Model

Amazon EMR gives you full control over compute clusters. You can provision, scale, and terminate these clusters manually or automate them via scripts or AWS Step Functions. EMR supports both transient and long-running clusters, making it suitable for jobs that require precise resource tuning or custom configurations.

AWS Glue, on the other hand, is serverless. You don’t manage infrastructure. You simply define jobs (usually in PySpark or Python), and AWS takes care of provisioning the compute, running the job, and tearing down resources afterward.

Summary:

• EMR = full control, requires more operational overhead.
• Glue = zero infrastructure management, ideal for quick ETL pipelines.

2. Flexibility and Customization

EMR is highly customizable. You can choose from various open-source engines (Spark, Hive, Hadoop, Presto, Trino), configure bootstrap actions, set custom networking, install libraries, and tune memory and CPU resources.

Glue offers limited flexibility. While Glue 3.0+ supports many Spark features, it restricts low-level configurations. It’s designed for standard ETL pipelines and batch jobs, not complex or deeply customized environments.

Summary:

• EMR = maximum flexibility.
• Glue = streamlined, but limited customization.

3. Supported Languages and Engines

EMR supports multiple engines including Apache Spark, Hive, Hadoop MapReduce, Presto, and Trino. You can develop in Scala, Python, SQL, or R, depending on the engine.

Glue primarily supports PySpark (via Spark) and Python (via Ray or the new Glue for Python shell). It also integrates well with the AWS Glue Data Catalog and supports visual jobs in Glue Studio.

Summary:

• EMR = multiple engines, full language support.
• Glue = PySpark and Python-focused.

4. Cost and Pricing Model

EMR is billed per EC2 instance per second. If you don’t terminate idle clusters, costs can increase quickly. However, you can optimize costs by using Spot Instances or EMR Serverless (a newer option).

Glue is billed per Data Processing Unit (DPU) per second. It’s more cost-effective for lightweight and intermittent ETL workloads since it automatically scales and shuts down after job completion.

Summary:

• EMR = potentially cost-effective for heavy, continuous workloads.
• Glue = efficient for on-demand or infrequent processing.

5. Use Case Suitability

Use Case	Recommended Tool
Scheduled ETL pipeline with simple logic	AWS Glue
Heavy Spark jobs with advanced tuning	Amazon EMR
Machine learning with distributed training	Amazon EMR
Integration with Data Catalog and Lake Formation	AWS Glue
Real-time log analysis with Presto or Trino	Amazon EMR
Lightweight jobs triggered by events (e.g., S3 PUT)	AWS Glue

6. Ease of Getting Started

EMR requires configuration: you must provision a cluster, choose instance types, define security roles, and manage network settings. While EMR Studio simplifies some of this, there’s still a learning curve.

Glue is easy to start with. You can create a job from the AWS Console, use a built-in data catalog, and execute your ETL logic with minimal configuration.

Summary:

• EMR = more setup, more control.
• Glue = faster time-to-value for standard ETL.

Conclusion

Both EMR and Glue are powerful tools for processing large-scale data on AWS, but they serve different purposes:

• Choose Amazon EMR when you need full control over infrastructure, support for various engines, and high customization.
• Choose AWS Glue when you want a fully managed, serverless, easy-to-use platform for ETL and data cataloging.

Understanding your workload’s complexity, performance needs, and operational model will help you choose the right service for your data architecture.

If you’re just getting started with AWS data processing, consider prototyping in Glue for quick wins, and migrating to EMR for more complex or performance-critical pipelines.