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AWS Glue

AWS Glue provides a shared Data Catalog plus managed serverless data-integration capabilities including crawlers, batch and streaming ETL jobs, visual authoring, data quality, and workflow features.

Explore pricing models, common use cases, infrastructure support, and the AWS services that commonly work with AWS Glue.

AWS Glue pricing and cost programs

Pricing model: Serverless data integration usage

On-Demand
Available
Reserved Instances or reserved capacity
Not applicable
Savings Plans
Not applicable
Spot
Not applicable

Billing dimensions: DPU time · Data catalog requests and storage · Data quality · Interactive sessions

Programs and modes: ETL jobs · Flex execution · Data Catalog · Crawlers and Data Quality

Job type, worker class, execution class, and catalog usage affect charges.

Free Tier: Service-specific — verify current offers

Pricing reviewed 2026-07-25. Reviewed against the linked official AWS pricing page. Recheck regional rates and program terms before purchase.

Official AWS pricing

Official AWS sources reviewed 2026-07-21.

Why implement AWS Glue?

  • Centralizes table and partition metadata used by Athena, EMR, Redshift Spectrum, Lake Formation, and other compatible engines.
  • Runs Spark, Python shell, Ray, and supported streaming data-integration workloads without maintaining their underlying job clusters.
  • Adds crawlers, schema inference, connectors, bookmarks, visual ETL, workflows, lineage and data-quality capabilities for repeatable data pipelines.

How to implement AWS Glue

  1. Define raw, staged, and curated data contracts, file and table formats, partitions, ownership, quality rules, retention, lineage, and Lake Formation or IAM authorization before cataloging assets.
  2. Create least-privilege job and crawler roles, encrypted connections and temporary storage, catalog databases and tables through controlled definitions or carefully configured crawlers, and protect production schemas from unwanted crawler changes.
  3. Author versioned jobs with explicit worker type, capacity, dependencies, bookmarks or checkpoints, retries, timeouts, metrics and logs; test schema drift, duplicates, partial writes, job restart, skew, and reconciliation before scheduling.

AWS Glue best practices

  • Treat the Data Catalog as governed production metadata: assign owners, constrain crawler targets and change behavior, separate environments, and review inferred schemas rather than accepting every mutation automatically.
  • Use columnar output, partition pruning, predicate pushdown, efficient file sizes, incremental processing, appropriate worker types, and Spark tuning based on job metrics instead of adding capacity by default.
  • Use separate least-privilege roles and connections, encrypt data and logs, keep secrets out of scripts, apply Lake Formation where appropriate, publish data-quality results, and design idempotent commits plus recovery from partially written output.

AWS Glue use cases and server impact

  • Serverless ETL and data-lake curation
  • Shared analytics metadata catalog
  • Schema discovery, data quality, and streaming transformations

Replaces many ETL worker clusters, metastore servers, and crawler schedulers, while pipeline code, schema governance, data quality, job efficiency, and target consistency remain customer-owned.

Official implementation resources

Commonly paired AWS services

Planning guides that use AWS Glue