AWS Glue
Data virtualization software
Big data integration platforms
ETL tools
Data preparation software
Machine learning data catalog software
Data replication software
Backup software
Data integration tools
Cloud data integration software
Data recovery software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if AWS Glue and its alternatives fit your requirements.
Pay-as-you-go
Free Trial unavailable
Free version
Small
Medium
Large
- Accommodation and food services
- Retail and wholesale
- Arts, entertainment, and recreation
What is AWS Glue
AWS Glue is a managed, serverless data integration service used to discover, prepare, and move data for analytics and machine learning workloads. It provides a centralized Data Catalog, visual and code-based ETL development, and execution engines based on Apache Spark and Python shell jobs. Typical users include data engineers and analytics teams building pipelines across AWS data lakes, warehouses, and operational data sources. The service is tightly integrated with other AWS services for storage, security, and monitoring.
Serverless ETL execution
Glue runs ETL jobs without requiring customers to provision or manage clusters, which reduces infrastructure administration compared with self-managed integration stacks. It supports both Spark-based jobs and lightweight Python shell jobs for different transformation needs. Capacity scales through managed compute options, and job execution integrates with AWS identity and monitoring services. This model fits teams standardizing on AWS-managed services.
Integrated Data Catalog
AWS Glue Data Catalog provides a managed metadata repository for datasets, schemas, and partitions, and it is commonly used as a shared catalog across AWS analytics services. Crawlers can infer schema from supported data stores and update table definitions over time. The catalog integrates with AWS Lake Formation and IAM for access control patterns. This reduces duplicated metadata management across ETL, query, and ML workflows.
Multiple development interfaces
Glue supports visual job authoring (Glue Studio) as well as code-first development using PySpark/Scala and Python. It offers built-in transforms and connectors for common AWS sources/targets, and it can connect to JDBC-accessible systems via connections. Workflows and triggers support basic orchestration of dependent jobs. This flexibility helps teams align with different skill sets and governance requirements.
AWS-centric architecture
Glue is designed primarily for AWS environments, and many operational patterns assume AWS-native storage, security, and networking. While it can connect to external systems (for example via JDBC), cross-cloud and on-prem connectivity often requires additional networking setup and may introduce latency and operational complexity. Organizations pursuing vendor-neutral integration may find portability limited. This can affect long-term architecture choices if multi-cloud is a requirement.
Not true data virtualization
Glue focuses on batch/stream ETL and metadata management rather than providing a full data virtualization layer for real-time federated queries across heterogeneous sources. For use cases that require live query federation, semantic virtualization, or query optimization across many systems, Glue typically needs to be paired with separate query/federation technologies. As a result, it may not satisfy “single logical view” requirements by itself. This distinction matters when the primary goal is virtualization rather than pipeline execution.
Cost and tuning complexity
Job performance and cost depend on configuration choices such as worker type, number of workers, job bookmarks, and partitioning strategies. Debugging Spark-based jobs can require specialized skills, and iterative tuning may be needed to control runtime and spend. Some advanced transformations still require custom code rather than purely visual configuration. Teams without Spark experience may face a learning curve.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: Data Catalog free tier: first 1,000,000 metadata objects stored per month and first 1,000,000 metadata requests per month are free. No time-limited trial documented on the AWS Glue pricing page.
Key usage prices & notes (as listed on AWS Glue official pricing page):
- ETL jobs, AWS Glue Studio interactive sessions, and Crawlers: $0.44 per DPU-hour (billed per second, 1-minute minimum). Example: a 15-minute job using 6 DPU = 6 * 0.25 * $0.44 = $0.66.
- Flexible execution ("Flex") option for non-SLA workloads: example shown at $0.29 per DPU-hour (used in the Data Quality example as the Flex rate).
- AWS Glue Crawlers: billed at $0.44 per DPU-hour (examples provided on page).
- Data Catalog (metadata storage & requests):
- Storage: first 1,000,000 metadata objects free; over 1,000,000 objects charged $1.00 per 100,000 objects over the free million, per month.
- Requests: first 1,000,000 metadata requests per month free; example states billing for 1,000,000 requests above free tier = $1 (i.e., $1 per 1,000,000 requests as example on the page).
- Data Catalog compute tasks (optimization, statistics generation, materialized view refresh, compaction): $0.44 per DPU-hour (billed per second with 1-minute minimum); materialized view refresh and optimization examples listed at $0.44/DPU-hour.
- AWS Glue DataBrew:
- Interactive sessions: $1.00 per 30-minute session (billed per session; examples on page describe session counting behavior).
- DataBrew jobs: example node-hour rate shown as $0.48 per node-hour (example: 10-minute job consuming 5 nodes billed as 5 * (1/6 hour) * $0.48 = $0.40).
- AWS Glue Data Quality: requires a minimum of 2 DPUs for data-quality tasks (1-minute minimum billing); anomaly detection adds 1 DPU per statistic for detection time; statistics storage is free (limit: 100K statistics per account, retained for 2 years).
- AWS Glue Schema Registry: usage offered at no additional charge (no cost).
- Zero-ETL integrations: AWS states no additional AWS Glue fee for zero-ETL integration itself; you pay for source/target resources and Glue resources used to ingest data (charged based on data volume; each ingestion request has a 1 MB minimum).
Discounts / other notes:
- AWS states prices can vary by AWS Region (see regional pricing table on the AWS site). No subscription tiers or fixed monthly plans listed; pricing is usage-based. No explicit volume/commitment discounts described on the Glue pricing page (standard AWS commitments/enterprise agreements may apply via AWS generally, but not detailed on this page).
Examples from the official page (illustrative):
- ETL job: 15 minutes, 6 DPU -> 6 * 0.25 * $0.44 = $0.66.
- DataBrew interactive session: one 30-minute session = $1.00; multiple brief interactions may count as multiple sessions per page examples.
(Information sourced only from the AWS Glue official pricing page.)
Seller details
Amazon Web Services, Inc.
Seattle, Washington, USA
2006
Subsidiary
https://aws.amazon.com/
https://x.com/awscloud
https://www.linkedin.com/company/amazon-web-services/
