AWS DataZone governs who can discover and request access to data projects across teams. AWS Glue Data Catalog is the technical metastore that Athena, Redshift Spectrum, and EMR query directly. In June 2026, AWS shipped two separate fixes for AI-agent readiness, a Glue semantic-search preview and a new knowledge-graph service called AWS Context, because neither catalog shipped ready for AI agents.
This isn’t a which-one-wins comparison. DataZone and Glue Data Catalog solve different problems, and most AWS shops eventually run both. What follows covers what each one actually is, where they diverge across nine dimensions, and when to stand up one before the other. It also covers the sharper question the search results haven’t caught up to yet: whether either tool, or AWS’s own new fixes, is actually ready to hand context to an AI agent, and what happens the moment the data estate isn’t AWS-only. The gap between a data catalog built for humans and a context layer built for AI agents at runtime turns out to matter more than either console admits.
| Dimension | AWS DataZone | AWS Glue Data Catalog |
|---|---|---|
| What it is | Business-facing catalog with project- and domain-based access governance | Technical metastore that stores table/schema metadata for AWS analytics engines |
| What it does | Lets business users discover, request, and be granted access to data assets | Indexes the location, schema, and runtime metrics Athena, Redshift Spectrum, EMR, and Glue jobs read |
| Who owns it | Data governance / platform teams setting up domains and projects | Data engineering teams running crawlers, ETL jobs, and Spark workloads |
| Key strength | Access governance and self-service discovery for non-engineers | Direct, low-latency integration with AWS query and ETL engines |
| Best for | Cross-team governance, project-based access requests, business catalogs | Technical metadata backing existing AWS data-processing pipelines |
| Questions it answers | “Who can access this data, and how do they request it?” | “What tables and schemas exist, and how do my jobs find them?” |
| Cost model | Free tier (20 MB metadata, 4,000 API requests/month), pay-as-you-go beyond | Priced per crawler run and per API request; typically bundled into existing Glue usage |
AWS DataZone vs Glue Data Catalog: what’s the difference?
AWS DataZone and AWS Glue Data Catalog sit at different layers of the same AWS data estate. One governs who can access data, the other stores what the data technically is, and most of the confusion between them comes from expecting one tool to do the other’s job.
AWS Glue Data Catalog has existed since Glue’s general-availability launch in August 2017, storing table and schema metadata for AWS’s query engines. AWS DataZone reached general availability on October 4, 2023, according to AWS’s own announcement, about a year after its preview at re:Invent 2022, specifically to add a business-facing layer AWS’s technical catalogs never had.
DataZone is the governance and discovery layer: who decides, who requests, who approves. Glue Data Catalog is the technical layer: what tables exist, what schema they have, which engine can read them. Both are mediated through the same underlying AWS Lake Formation permission model and IAM, the same zero-trust access model that shows up across AWS’s governance stack, which is why a governance program built around AI agents has to account for both layers, not just one.
DataZone can also front a Glue Data Catalog data source directly, through a documented grant-access pattern. In practice, a reader often isn’t choosing DataZone over Glue Data Catalog. They’re choosing which one to stand up first, and that’s the practical question for most AWS shops. It matters more than either tool’s feature list alone.
What is AWS DataZone?
AWS DataZone is a business-facing data catalog that lets teams publish, discover, and request access to data assets through governed projects and domains, without engineers manually granting access table by table.
DataZone organizes work into projects, a team’s workspace, and domains, a business unit’s boundary. Access to any asset is requested and approved inside that structure, which is what makes DataZone useful for self-service discovery rather than a ticket queue.
According to AWS’s pricing page (2026), DataZone’s free tier covers 20 MB of metadata storage, 4,000 API requests, and 0.2 compute units per account per billing month, with pay-as-you-go pricing beyond that threshold. That structure signals a project-scale, incremental-adoption design, not an all-or-nothing platform buy.
DataZone now integrates with Glue Data Quality, according to AWS’s big data blog, surfacing data-quality scores directly inside a DataZone project page. That’s a sign AWS is actively knitting DataZone and Glue Data Catalog together, not treating them as separate product lines.
Core components of AWS DataZone
- Projects: a team’s workspace for a specific initiative or use case
- Domains: a business unit’s boundary for organizing projects and data
- Business glossary and metadata forms: shared vocabulary attached to assets, the same kind of structured metadata an AI agent needs to act on rather than just read
- Data source connections: including managed Glue Data Catalog assets
- Access-request workflow: the approval path business users go through
- Surfaced data-quality scores: pulled in from Glue Data Quality
For AWS-native teams, that access-governance model is the point. It says nothing about whether the data behind it is structured for an AI agent asking for it programmatically, not through a console.
What is AWS Glue Data Catalog?
AWS Glue Data Catalog stores table, schema, and partition metadata for Athena, Redshift Spectrum, EMR, and Glue ETL jobs. AWS calls it a centralized repository that acts as an index to the location, schema, and runtime metrics of your data sources.
Glue Data Catalog is populated by crawlers or explicit table definitions, according to AWS’s own documentation, and it’s read directly by AWS’s SQL and Spark engines. AWS’s crawlers reach data sources that are internal to AWS and external to it. What the catalog has never carried on its own is a discovery experience aimed at a business user, which is the job DataZone was built for.
As of June 2026, Glue Data Catalog added a business-context and semantic-search preview, according to AWS’s announcement: glossary terms, custom metadata fields, and “skill assets” that let MCP-compatible agents retrieve domain context directly from the catalog. It’s real, but it’s a preview, live in only four regions (US East N. Virginia, US East Ohio, US West Oregon, and Europe Ireland).
Glue Data Catalog maintains a record of the transformations and operations performed on your data, which AWS calls data lineage information. That is not the same as the lineage DataZone or a downstream consumer actually needs. Full OpenLineage-based lineage runs through DataZone and SageMaker Unified Studio, and AWS states the OpenLineage libraries for Spark “are built into AWS Glue v5.0+ for Spark DataFrames only. Dynamic DataFrames are not supported,” per AWS’s lineage documentation. For a deeper look at where the technical catalog falls short on its own, see AWS Glue Data Catalog: 6 key limitations and fixes.
Core components of AWS Glue Data Catalog
- Tables and databases: the schema-level metadata AWS engines query
- Crawlers: automated jobs that populate the catalog from raw data
- Glue Schema Registry: schema versioning for streaming data
- Business-context and semantic-search preview: glossary terms and custom fields, the kind of grounding a data catalog needs to work as an LLM knowledge base rather than a static reference table
- Skill assets: retrieval endpoints built for MCP-compatible AI agents
- OpenLineage-dependent lineage path: Spark DataFrames only, via Glue 5.0
The catalog answers what exists, not whether an AI agent can trust and act on it. That’s exactly the gap AWS spent June 2026 trying to close.
AWS just rewrote its own data stack playbook
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Download the 2026 ReportAWS DataZone vs Glue Data Catalog: head-to-head comparison
The sharpest differences between AWS DataZone and AWS Glue Data Catalog show up in who they’re built for and how they handle governance, lineage, and, increasingly, AI-agent access. They don’t show up in raw feature counts.
AWS itself publishes a comparison of the two services, but it stops at feature parity. The table below adds two dimensions no ranking page on this topic, including AWS’s own, currently covers: AI-agent readiness and cross-cloud reach.
| Dimension | AWS DataZone | AWS Glue Data Catalog |
|---|---|---|
| Primary focus | Business-facing discovery and access governance | Technical metastore for query/ETL engines |
| Key stakeholder | Data governance / business analysts | Data engineering |
| Access model | Project- and domain-based requests, mediated by Lake Formation/IAM | Direct IAM permissions on catalog resources |
| Native lineage | Yes, when connected to a Glue-cataloged source | A record of transformations and operations; full OpenLineage lineage ships in Glue 5.0+ for Spark DataFrames only, not Dynamic DataFrames |
| AI-agent readiness (as of 2026-09-19) | Not documented as of 2026-09-19 | Business-context and semantic-search preview, four Regions |
| Cross-cloud reach (as of 2026-09-19) | Snowflake data source indexes databases, schemas, and column-level lineage; one database and schema per connection, set up through the AWS CLI | Catalog federation reaches remote Iceberg catalogs, limited to Iceberg tables stored in Amazon S3 |
| Time to value | Fast for a single project; scales with domain/governance setup | Immediate once crawled; no separate discovery layer to configure |
| Tooling requirements | DataZone console/API, connected data sources | Crawlers or explicit table definitions, IAM policies |
| Failure mode | Often governance overhead, if projects/domains aren’t scoped to real teams | Built for query engines, so business users need DataZone or another catalog on top |
Consider a team building an AWS-native analytics platform. They stand up Glue Data Catalog first because Athena and Redshift Spectrum jobs, the same query layer behind governed KPIs in a Databricks Unity Catalog setup, need it on day one. DataZone comes later, once business analysts start asking to self-serve access instead of filing IAM tickets.
That scenario gets more complicated the moment the same company also runs a Snowflake warehouse for a different business unit. Both AWS tools can now see part of it: Glue Data Catalog federates to remote Iceberg catalogs, and DataZone connects Snowflake as a data source. What crosses is a pointer and a shape. The Snowflake side still owns its own access story, and the glossary terms and ownership that make an asset usable stay where they were defined.
Every dimension in that table describes a decision made from inside AWS, looking out. The moment a second cloud enters the picture, the table stops answering the reader’s actual question.
How do AWS DataZone and AWS Glue Data Catalog work together?
In practice, most AWS-native teams don’t choose between AWS DataZone and AWS Glue Data Catalog. DataZone is designed to govern access to Glue-cataloged data, not replace it.
DataZone project backed by a Glue data source
A DataZone project requests and is granted access to Glue Data Catalog tables through AWS’s documented grant-access flow, so governance and the metastore stay in sync.
DataZone surfacing Glue Data Quality scores
Quality checks that run in Glue Data Quality now appear directly on the DataZone project page, according to AWS’s big data blog. Business users see trust signals without leaving the discovery UI.
Query engines reading straight through
Athena and Redshift Spectrum query Glue Data Catalog directly, regardless of DataZone. DataZone governs who can request that access, not how the query itself runs.
DataZone and Glue Data Catalog both stop at the same AWS Lake Formation/IAM boundary.
Start with Glue Data Catalog when engineers already run Athena, Spark, or EMR jobs and need a working technical metastore now. Start with DataZone when business users need self-service discovery and project-based access governance across teams before engineering has a backlog for manual IAM grants. Invest in both simultaneously for a greenfield AWS lakehouse build where technical query engines and business-facing discovery need to launch together. Context layer evaluation criteria covers a broader framework for once “both AWS tools” isn’t the full answer either.
That pattern, a governance layer bolted onto a technical metastore, repeats on every cloud platform. Snowflake’s own Horizon governance work and Databricks’ Genie ontology follow the same shape. That’s exactly the gap a cross-cloud context layer exists to close, and stitching AWS, Snowflake, and Databricks tools together by hand is one way teams try to build that layer themselves, before they decide whether to buy one instead.
Find the gap before an AI agent does
Map where your catalog setup already has AI-agent context and where it still depends on a human to fill in the blanks.
Run the Context Gap CheckAre AWS DataZone and AWS Glue Data Catalog ready for AI agents?
As of June 2026, AWS itself answered this question by shipping two separate fixes. That’s proof neither AWS DataZone nor AWS Glue Data Catalog was built with AI-agent retrieval in mind.
Glue’s business-context and semantic-search preview adds glossary terms, custom metadata fields, and skill assets that let MCP-compatible agents, including coding assistants like Claude Code, Kiro, Cursor, and Codex, retrieve domain context directly from the catalog. It’s effectively a semantic layer for AI agents in miniature, the kind of retrieval layer enterprises are already building for Bedrock-based agents. AWS’s own preview post names Claude Code, Kiro, Cursor, and Codex as MCP-compatible agents that read Glue’s business context through the aws-data-analytics plugin.
AWS Context, announced the same month, goes further. According to AWS’s ML blog, it maps relationships across an organization’s existing data into a knowledge graph for AI agents and integrates with AWS Glue Data Catalog, Amazon SageMaker Unified Studio, and AWS Lake Formation. AWS’s own post lists the integrations by name, Glue Data Catalog, SageMaker Unified Studio, Lake Formation, S3 Iceberg tables, Athena, Redshift, Amazon Quick, Bedrock AgentCore, and EKS, and describes AWS Context itself as a shared, governed context layer. Its status on that post is “coming soon,” not generally available.
Both fixes are AWS-native services. The catalogs underneath them are not confined the same way, and AWS documents three ways they already read outward. Those are two different boundaries, and keeping them apart is the whole argument.
Glue Data Catalog added catalog federation to remote Iceberg catalogs in November 2025, with named integration for Snowflake Polaris Catalog, Databricks Unity Catalog, and other custom catalogs supporting Iceberg REST specifications. It is generally available, in all AWS commercial Regions where AWS Glue and Lake Formation are available. Amazon DataZone supports Snowflake as a third-party data source, indexing Snowflake databases and schemas and capturing column-level lineage from Snowflake’s query history. And AWS documents metadata synchronization with third-party business data catalogs for SageMaker Unified Studio, covering projects, assets, descriptions, glossary terms, and their hierarchies. All three carry a qualifier, and the qualifiers point the same direction.
| AWS capability | What it carries across | The qualifier |
|---|---|---|
| Glue catalog federation to remote Iceberg catalogs | Databases and tables from Snowflake Polaris Catalog, Databricks Unity Catalog, or any Iceberg REST catalog | AWS’s own wording is “remote Iceberg tables, stored in Amazon Simple Storage Service (Amazon S3).” A Snowflake-native or Delta table that isn’t Iceberg-on-S3 stays out of reach |
| DataZone Snowflake data source | Snowflake databases and schemas, plus column-level lineage read from query history | Created through the AWS CLI, one database and one schema per connection, and it reads inward only |
| Third-party business data catalog sync for SageMaker Unified Studio | Projects, assets, descriptions, glossary terms, and their hierarchies | The one bidirectional path AWS documents, and it runs through a catalog that isn’t AWS’s |
So the query boundary moved outward and the business-context boundary did not. Glue’s business-context preview and AWS Context, the two surfaces an AI agent actually reasons over, are still AWS-native services mediated through the same Lake Formation/IAM model. An agent can now reach a federated Snowflake table through Glue and find no glossary term, no owner, and no certification state attached to it. That is the same build-vs-buy-vs-bundle question applied to AWS’s whole bundled stack rather than to one tool, and AWS’s own documented answer for keeping business context aligned across platforms is a catalog it doesn’t own.
| Feature | Status (as of 2026-09-19) | Scope |
|---|---|---|
| Glue business-context / semantic search | Preview, launched 17 June 2026 | 4 AWS Regions: N. Virginia, Ohio, Oregon, Ireland |
| AWS Context (knowledge-graph service) | Announced 17 June 2026; AWS’s own post says “coming soon” | AWS-native; integrates with Glue Data Catalog, SageMaker Unified Studio, Lake Formation, Athena, Redshift, and Amazon Quick |
| DataZone native AI-agent support | Not documented as of 2026-09-19 | Not documented as of 2026-09-19 |
AWS is closing its AI-agent gap fast. The reach of its catalogs has grown with it. The business context an agent needs is the part that still has one home per service.
How Atlan approaches AWS DataZone and Glue Data Catalog
The wall with AWS DataZone or Glue Data Catalog tends to arrive when a data estate stops being AWS-only. That’s exactly the boundary AWS’s own June 2026 fixes don’t cross either.
Organizations that govern access and metadata purely through DataZone and Glue Data Catalog get AWS-native governance that works well inside AWS. The native catalogs now reach outward for tables and lineage, in the narrow ways described above. The business context does not follow them out, and AWS Context, the service built to give agents that context, is scoped to AWS’s own Lake Formation/IAM model. The distinction between a data catalog and the enterprise context layer is exactly this: one is scoped to where the data was born, the other is scoped to wherever an AI agent needs to reason about it.
Atlan’s context layer reads schema, lineage, and ownership from AWS Glue Data Catalog and DataZone alongside Snowflake, BigQuery, Redshift, Databricks, and on-prem sources. An AI agent gets the same governed, policy-aware context regardless of which cloud’s catalog the underlying data sits in. Atlan only reads context: schema, lineage, ownership, timestamps. It never touches or moves the underlying data, which matters to the same access-governance-conscious reader who was evaluating DataZone’s project model in the first place. That’s the same agent context layer architecture behind the approach, worth weighing against AWS’s own bundled stack of Glue, DataZone, and Context before building the AI agent harness that actually consumes this context.
Multi-cloud catalog fragmentation is common enough that most enterprise data teams have felt it firsthand: one team standardizes on AWS, another lands on Snowflake for a specific workload, and nobody owns the seam between them. Closing that seam is what a multi-cloud context layer is built to do, and a reference architecture for one shows what it looks like in practice, rather than as a slide.
Is your catalog actually ready for AI agents?
Run your AWS, Snowflake, or Databricks catalog setup against a practical readiness checklist before an agent finds out the hard way.
Take the Readiness CheckWhy the AWS boundary is the real decision here
The real question was never DataZone or Glue Data Catalog. Most AWS shops end up running both, at different layers, and the decision framework above should make that call faster, not harder.
The sharper question is what neither tool, nor AWS’s own June 2026 work, solves yet: AI-agent context that survives contact with a real multi-cloud stack. AWS is closing its AI-agent gap fast, and its catalogs now read well past their own boundary, to Snowflake Polaris and Databricks Unity through Glue federation and to 12 external sources through SageMaker Lakehouse. What crosses is tables. Glue’s semantic-search preview and AWS Context, the two surfaces an agent reasons over, run inside the same Lake Formation and IAM model, so the glossary term, the owner, and the certification state stay behind.
For a team running Snowflake or Databricks alongside AWS, that boundary is exactly where the decision starts, not ends. Implementing an enterprise context layer for AI is the practical next step once the AWS-only decision has been made.
What does stitching this together yourself actually cost?
Weigh the engineering time of bridging AWS, Snowflake, and Databricks by hand against a context layer built to span all three.
Run the ROI CalculatorFAQs about AWS DataZone vs Glue Data Catalog
1. AWS DataZone vs Glue Data Catalog: which one do I need?
Most AWS-native teams need both, at different layers. Use Glue Data Catalog if you already run Athena, Spark, or EMR jobs that need a technical metastore, and add DataZone once business users need self-service discovery and project-based access requests instead of manual IAM grants.
2. What’s the difference between AWS DataZone and AWS Lake Formation?
Lake Formation is the underlying permission engine that enforces fine-grained access on S3 and Glue-cataloged data. DataZone is the business-facing layer built on top of it, where users request and get granted access through projects and domains rather than dealing with IAM policies directly.
3. Is AWS Glue Data Catalog the same as a data catalog?
It started as a technical metastore for AWS query engines rather than a business-facing catalog. Glue’s glossary terms shipped in June 2026 as a preview in four Regions, alongside custom metadata fields and semantic search. Self-service discovery and access requests still come from pairing it with DataZone.
4. Does AWS Glue Data Catalog track data lineage?
AWS documents the Data Catalog maintaining a record of the transformations and operations performed on your data. Full OpenLineage-based lineage, the kind DataZone consumes, ships in Glue 5.0+ for Spark DataFrames only; AWS states Dynamic DataFrames are not supported.
5. What is AWS Glue Data Catalog’s business context and semantic search feature?
It’s a preview launched on 17 June 2026 that adds glossary terms, custom metadata fields, and skill assets so MCP-compatible AI agents can retrieve domain context directly from the catalog, with a SearchAssets API for finding assets by business meaning. It’s live in four AWS Regions: N. Virginia, Ohio, Oregon, and Ireland.
6. Can AI agents query AWS Glue Data Catalog directly?
Yes, through the skill-assets feature in Glue’s business-context preview, which is built for MCP-compatible agents. AWS Context, announced the same month, aims to go further by mapping relationships across an organization’s data into a knowledge graph, integrated with Glue Data Catalog, SageMaker Unified Studio, and Lake Formation, though AWS’s own June 2026 post describes it as coming soon rather than generally available.
7. What is Amazon DataZone’s pricing?
DataZone has a free tier covering 20 MB of metadata storage, 4,000 API requests, and 0.2 compute units per account per billing month, with pay-as-you-go pricing beyond that threshold.
Sources
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- Channy Yun (2023). Amazon DataZone Now Generally Available, AWS News Blog. https://aws.amazon.com/blogs/aws/amazon-datazone-now-generally-available-collaborate-on-data-projects-across-organizational-boundaries
- Amazon DataZone Pricing, AWS. https://aws.amazon.com/datazone/pricing/
- AWS Glue Data Catalog and Crawler Overview, AWS Documentation. https://docs.aws.amazon.com/glue/latest/dg/catalog-and-crawler.html
- AWS Glue Data Catalog Now Supports Business Context and Semantic Search (Preview), AWS What’s New. https://aws.amazon.com/about-aws/whats-new/2026/06/aws-glue-data-catalog/
- Mai-Lan Tomsen Bukovec (2026). Context Intelligence for Your Data and AI Agents at Scale, AWS Machine Learning Blog. https://aws.amazon.com/blogs/machine-learning/context-intelligence-for-your-data-and-ai-agents-at-scale/
- Getting Started with Business Context in the AWS Glue Data Catalog, AWS Documentation. https://docs.aws.amazon.com/glue/latest/dg/catalog-business-context-getting-started.html
- Automate Lineage Capture from Tools, Amazon SageMaker Unified Studio, AWS Documentation. https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/datazone-data-lineage-automate-capture-from-tools.html
- Amazon DataZone Now Integrates with AWS Glue Data Quality, AWS Big Data Blog (2024). https://aws.amazon.com/blogs/big-data/amazon-datazone-now-integrates-with-aws-glue-data-quality-and-external-data-quality-solutions/
- Grant Access to Managed Glue Data Catalog Assets in Amazon DataZone, AWS Documentation. https://docs.aws.amazon.com/datazone/latest/userguide/grant-access-to-glue-asset.html
- Amazon DataZone vs AWS Glue Data Catalog, AWS. https://d1.awsstatic.com/datazone-assets/Amazon-DataZone-AWS-Glue-Data-Catalog.pdf
- Federated Catalogs in SageMaker Lakehouse, AWS Documentation. https://docs.aws.amazon.com/sagemaker-lakehouse-architecture/latest/userguide/federated-catalogs.html
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- AWS Glue Catalog Federation to Remote Apache Iceberg Catalogs, AWS What’s New (November 24, 2025). https://aws.amazon.com/about-aws/whats-new/2025/11/aws-glue-catalog-federation-remote-apache-iceberg-catalogs
- Debika D, Pratik Das and Srividya Parthasarathy (2025). Introducing Catalog Federation for Apache Iceberg Tables in the AWS Glue Data Catalog, AWS Big Data Blog. https://aws.amazon.com/blogs/big-data/introducing-catalog-federation-for-apache-iceberg-tables-in-the-aws-glue-data-catalog/
- Connect Snowflake as a Data Source in Amazon DataZone, AWS Documentation. https://docs.aws.amazon.com/datazone/latest/userguide/snowflake-data-source.html
- Third-Party Business Data Catalog Integrations, Amazon SageMaker Unified Studio, AWS Documentation. https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/third-party-catalog-integrations.html
