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Amazon DataZone

Amazon DataZone is a business data catalog and governed self-service workflow for discovering, describing, publishing, requesting, approving, and consuming data assets across organizational domains and projects.

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

Amazon DataZone pricing and cost programs

Pricing model: Data-management usage

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

Billing dimensions: Requests · Metadata storage · Compute units · AI recommendations

Programs and modes: Pay-as-you-go requests · Metadata storage · Compute

DataZone charges are usage-based and exclude charges from connected data services.

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 Amazon DataZone?

  • Adds business names, descriptions, owners, glossaries, lineage context, and discoverability to technical assets so consumers can find trusted data without knowing its storage account first.
  • Provides project boundaries and subscription approval workflows that connect data producers, stewards, and consumers through auditable access decisions.
  • Integrates supported AWS Glue and Redshift assets, environment blueprints, identity federation, cross-account associations, and automated permission fulfillment for managed assets.

How to implement Amazon DataZone

  1. Define domain boundaries, operating model, producer and consumer personas, ownership, glossary standards, approval policy, account associations, and identity source before creating the DataZone domain.
  2. Create projects and environment profiles, connect authorized data sources, run metadata-generation jobs, assign owners and business metadata, curate assets, and publish approved versions to the catalog.
  3. Configure subscription approvers and fulfillment, test search-to-access workflows for Glue and Redshift assets, capture CloudTrail evidence, and establish freshness, certification, deprecation, revocation, and incident procedures.

Amazon DataZone best practices

  • Treat DataZone as a governance and discovery layer rather than a data-movement service; keep the authoritative data, metadata, access controls, and quality processes aligned across underlying systems.
  • Use least-privilege domain and project roles, IAM Identity Center for workforce access where appropriate, encrypted dependent resources, explicit cross-account associations, and CloudTrail monitoring.
  • Require accountable owners, concise business definitions, useful classification and quality signals, time-bounded approvals where policy requires them, and regular cleanup of stale assets and subscriptions.

Amazon DataZone use cases and server impact

  • Enterprise data discovery and business catalog
  • Governed self-service access to analytics assets
  • Cross-account data-product publishing and subscription workflows

Replaces portions of custom data-catalog portals and access-request workflows, but it does not host or move the underlying datasets and cannot replace data stewardship or source-system controls.

Official implementation resources

Commonly paired AWS services