AWS Glue and Amazon EMR both run Apache Spark workloads, but they target different operational models. Glue is serverless ETL. EMR is managed cluster infrastructure. For AI/ML data pipelines, the choice affects cost, control, and operational complexity.

Overview

AspectAWS GlueAmazon EMR
Operational ModelServerlessManaged clusters (or serverless)
Primary UseETL and data integrationGeneral-purpose big data processing
Spark SupportPySpark, Spark SQLFull Spark ecosystem
Other EnginesNoneHive, Trino/Presto, Flink, HBase, Hudi, Iceberg, etc.
Cluster ManagementNone (serverless)You manage (EC2/EKS), or none (EMR Serverless)
Data CatalogBuilt-in Glue Data CatalogUses Glue Data Catalog
Job AuthoringVisual + codeCode (notebooks, scripts)
PricingPer-DPU-hourPer-instance-hour, or per vCPU-hour and GB-hour (Serverless)

Architecture

Glue abstracts away cluster management entirely. You define jobs (visual or code), specify the number of DPUs (data processing units), and Glue handles provisioning, scaling, and teardown. Glue jobs run on a managed Apache Spark environment with automatic retries and job bookmarking for incremental processing. The current engine is AWS Glue 5.1 (announced November 2025), which runs Apache Spark 3.5.6 on Python 3.11 and Java 17 and adds Apache Iceberg format version 3.0 support plus AWS Lake Formation fine-grained access control for write operations. AWS Glue 5.0 (December 2024) remains widely used and integrates with Amazon SageMaker Unified Studio and SageMaker Lakehouse.

EMR gives you full cluster control. You choose instance types, cluster size, applications to install, and configuration parameters. EMR on EC2 provides maximum control. EMR on EKS runs Spark on your Kubernetes clusters. EMR Serverless provides a serverless option that competes more directly with Glue. EMR on Outposts extends EMR to on-premises hardware. Recent EMR releases ship Apache Spark 4.x (the emr-spark-8.0.0 release line) alongside the long-supported Spark 3.5 builds, so you can pick a Spark major version per workload.

Data Cataloging

Glue Data Catalog is a shared metadata repository that both services use. Glue crawlers automatically discover schema and create catalog tables. EMR jobs read and write through the same catalog. This shared catalog means you can use Glue for data discovery and cataloging while running heavy processing on EMR.

Feature Engineering for ML

For ML feature engineering, both services run Spark-based transformations. Glue is simpler for scheduled ETL jobs that prepare training data - daily feature aggregations, data cleaning, and dataset generation. The visual editor lets analysts build transformations without writing code.

EMR is better for complex feature engineering that needs custom libraries, GPU instances for deep learning preprocessing, or multiple processing engines. EMR notebooks provide an interactive environment for exploratory feature development before productionizing in pipelines.

Cost Comparison

Glue charges per DPU-hour (one DPU is 4 vCPUs and 16 GB of memory), billed per second with a 1-minute minimum on Glue 2.0 and later. Spark ETL jobs default to a minimum of 2 DPUs. For non-urgent batch work, Glue Flex execution runs at a lower per-DPU-hour rate (roughly a third less than the standard rate) in exchange for delayed, best-effort start times. For short, scheduled ETL jobs, Glue is cost-effective because you pay only for processing time. Confirm current per-DPU-hour rates on the AWS Glue pricing page, since they vary by Region.

EMR on EC2 prices the EMR layer on top of the underlying Amazon EC2 and Amazon EBS costs, with per-second billing. For long-running clusters or large-scale processing, EMR can be significantly cheaper, especially with Reserved Instances, Savings Plans, or Spot Instances (Spot can reach up to a 90 percent discount versus On-Demand). EMR Serverless offers consumption-based pricing similar to Glue: you pay for aggregate vCPU-hours and memory GB-hours consumed by workers (plus storage above the free 20 GB per worker), rounded up to the nearest second with a 1-minute minimum.

For bursty, scheduled workloads, Glue wins on cost. For sustained, large-scale processing, EMR on EC2 with Spot Instances and reserved capacity is usually materially cheaper than Glue, because you can keep utilization high and capture deep Spot discounts. Always model both against your real job profile before committing.

When to Choose Glue

Choose Glue for scheduled ETL jobs, data catalog management, and simple-to-moderate Spark transformations. Glue excels when you want zero infrastructure management, when your jobs run on a schedule (hourly, daily), and when the visual editor helps non-engineers contribute to data pipelines. Glue is the default choice for data preparation steps in ML pipelines on AWS.

When to Choose EMR

Choose EMR when you need full control over the processing environment, when you run multiple engines beyond Spark, when GPU instances are required, or when cost optimization through Spot Instances is important at scale. EMR is also the better choice for interactive data exploration and when your processing jobs run continuously rather than on a schedule.

Practical Recommendation

For ML data pipelines, start with Glue for data preparation and feature engineering jobs. Move to EMR when jobs become too large for Glue’s cost model, when you need GPU processing, or when you need engines beyond Spark. Many production architectures use both: Glue for cataloging and light ETL, EMR for heavy processing and interactive development.

Related comparisons: /comparisons/databricks-vs-emr/, /comparisons/dbt-vs-glue/, and /comparisons/delta-lake-vs-iceberg/.

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