AWS Glue Data Catalog is AWS’s serverless metadata store for services like Athena, Redshift Spectrum, and EMR. It catalogs data well inside AWS, and since November 2025 can even query remote Iceberg tables in Snowflake or Databricks. What it does not do is unify business context, policy, or AI agent access across those platforms.
That gap matters more as data estates spread across clouds. Flexera’s 2026 State of the Cloud Report found 89% of organizations already use more than one cloud provider, and 73% run hybrid cloud specifically. A catalog that only sees one cloud creates a blind spot exactly where AI agents need the most consistency: the boundary between systems. This piece compares what AWS Glue Data Catalog handles well against what a cross-cloud governance layer adds, and where the honest answer is that Glue alone is still enough.
| Criterion | AWS Glue Data Catalog | Cross-cloud governance layer |
|---|---|---|
| Cross-cloud scope | AWS-native; reads remote Iceberg catalogs (Snowflake, Databricks) via 2025 catalog federation, query only | Unifies metadata across AWS, Snowflake, Databricks, GCP, and on-prem systems |
| Business context | Technical metadata only; no built-in glossary or ontology | Business glossary, ontology, and semantic layer over technical metadata |
| Governance reach | Lake Formation permissions per AWS account; LF-Tags don’t propagate across accounts | Classification, policy, and stewardship rules propagate across the whole estate |
| AI agent access | No native agent-facing context API | Native MCP Server or context API serving governed context to agents |
| Setup and cost | Serverless, pay-per-crawler-run and DPU-hour, minimal setup inside AWS | Subscription platform; cost justified once data spans more than one cloud |
| Best fit | AWS-only teams and early-stage data platforms | Multi-cloud or hybrid enterprises running agents across systems |
What AWS Glue Data Catalog does well inside AWS
Permalink to “What AWS Glue Data Catalog does well inside AWS”AWS Glue Data Catalog is the persistent, serverless metastore behind most AWS analytics services. It stores table definitions and schema for data in S3, and Athena, Redshift Spectrum, and EMR all read from the same catalog, so a table cataloged once is queryable everywhere those services run. Crawlers discover schema automatically, a genuinely low-friction way to make S3 data queryable without hand-writing DDL for every table.
Glue also got materially better at reaching outside its own walls. As of November 2025, catalog federation lets AWS query engines like Athena, Redshift, and EMR read Iceberg tables cataloged in Snowflake Horizon, Snowflake Polaris, or Databricks Unity Catalog, without copying the underlying data. A Redshift analyst can now query a table that technically lives in Databricks and never leave the AWS console, part of a broader shift toward Iceberg as the shared table format across clouds.
None of this costs much to run. Glue is serverless, priced per crawler-runtime and per-DPU-hour, with no cluster to size or patch. For AWS-only teams, that combination of automatic discovery and low operational overhead is hard to beat on price. Inside a single cloud, Glue answers the discovery question well. The real question is what happens the moment an agent needs to reason about a table Glue has only queried, never governed.
Where does AWS Glue Data Catalog stop covering your data estate?
Permalink to “Where does AWS Glue Data Catalog stop covering your data estate?”Catalog federation extends what Athena or Redshift can query, but not what Glue governs. AWS’s own announcement describes federation as synchronizing query metadata and access permissions, with no mention of federating glossaries, lineage, or stewardship across the remote catalog. A Snowflake or Databricks table becomes queryable from AWS; it does not become governed by AWS, and its business meaning still lives wherever Snowflake Horizon, Unity Catalog metrics, or Genie Ontology define it.
The account boundary is the sharper problem. Lake Formation permissions and LF-Tags are scoped to the AWS account that created them, and consuming accounts must recreate their own tags rather than inheriting them. There is no single view of an organization’s governance posture across accounts, let alone across a multi-cloud context layer touching Snowflake or on-premises systems.
Business context is the other gap. Glue is, by design, a technical metadata store: schemas, data types, table locations, with no native glossary or semantic layer connecting a column like cust_id to the business definition of a customer. Lineage now runs through Amazon SageMaker Unified Studio, built on Amazon DataZone, capturing OpenLineage-compatible events from Spark jobs on EMR, Glue, and SageMaker notebooks, an improvement over the older DataZone-only path but still an AWS-and-Spark-specific view that says nothing about a value moving from Salesforce through dbt on Snowflake into a Tableau dashboard.
None of this makes Glue a bad catalog. It makes Glue an AWS catalog, doing what an AWS catalog is supposed to do, and the gap only shows up once agents have to reason across Snowflake, Databricks, or a warehouse Glue has never crawled.
What does a cross-cloud governance layer add beyond Glue?
Permalink to “What does a cross-cloud governance layer add beyond Glue?”A cross-cloud governance layer starts from a different premise: metadata is worth unifying regardless of which platform produced it. Atlan connects to AWS Glue, Redshift, and S3 the same way it connects to Snowflake, Databricks, dbt, Salesforce, and on-premises databases, pulling technical metadata from all of them into one Enterprise Data Graph rather than leaving each platform to catalog only what it can see.
On top of that sits business context Glue was never built to hold: a shared glossary, an ontology connecting terms across systems, and a semantic layer tying a column to the business concept it represents, generated and maintained by Atlan’s Context Agents rather than a wiki page nobody updates.
Governance is the second layer. Where LF-Tags stop at the AWS account boundary, a cross-cloud layer applies classification and stewardship consistently, so a column tagged sensitive in Snowflake carries the same restriction when a copy surfaces in Redshift, the difference between a data catalog built for AI and one built for a single cloud’s query engines.
The third layer is one Glue has no answer for: serving governed context to agents. Atlan delivers business context through MCP and a broader AI control plane, so an agent asking “what does active customer mean” gets the same answer from an AWS Bedrock agent or a Databricks Genie workflow. Glue can tell an agent what a table looks like; it cannot tell it whether the table’s definitions agree with the rest of the estate.
Why do AI agents need more context than a single-cloud catalog gives them?
Permalink to “Why do AI agents need more context than a single-cloud catalog gives them?”An agent that only sees AWS metadata answers AWS questions correctly and guesses at everything else. That is the practical cost of a catalog scoped to one cloud: not that it is wrong within its boundary, but that it has no signal once a question crosses it. A knowledge graph or context graph built only from Glue’s data inherits that boundary, however well-structured it is inside AWS.
This is why AI agent context has become its own design problem, separate from data infrastructure. Model Context Protocol gives agents a standard way to request context, but building MCP servers for enterprise data is only half the job if the server can only see one cloud. A model-agnostic context layer lets the same governed definitions reach a Bedrock agent, a Databricks Genie agent, or a custom LangGraph workflow without three separate integration projects, and the distinction between agent skills and MCP matters less than whether the context behind either one is consistent.
Enterprises running agents across more than one cloud, which per Flexera is most of them, are effectively asking one AI system to reason with several contradictory definitions of the same terms. A context layer vs. knowledge graph comparison matters here too: a graph gives an agent structure, but a governed context layer gives it one cross-cloud source for what that structure means.
AWS Glue Data Catalog vs. a cross-cloud governance layer: the detailed comparison
Permalink to “AWS Glue Data Catalog vs. a cross-cloud governance layer: the detailed comparison”The quick comparison above covers the headline differences. A few dimensions are worth walking through in more depth, because the honest answer is “it depends what you’re optimizing for,” not a clean win for either side.
| Dimension | AWS Glue Data Catalog | Cross-cloud governance layer |
|---|---|---|
| Discovery mechanism | Crawler-based schema inference, sampled for CSV/JSON | Connector-based ingestion across 80+ sources, plus crawlers |
| Lineage | Via SageMaker Unified Studio (OpenLineage), scoped to Spark on EMR, Glue, SageMaker | End-to-end across ingestion, transformation, and BI, regardless of cloud |
| Collaboration | No native annotation or stewardship workflow | Built-in comments, ownership, and stewardship on every asset |
| Data quality | Glue Data Quality rules, scoped to Glue-managed datasets | Aggregated signal across dbt tests, Great Expectations, native checks |
| Multi-account, multi-region | LF-Tags need independent setup per account; no unified GRC view | One policy enforced across accounts, regions, and clouds |
| Partner reach | Deepest AWS-native fit; Snowflake and Databricks reachable only via federation | Same governed view whether the source is Snowflake, Databricks, or AWS |
The lineage row is the clearest illustration of the “it depends” answer. If every job an enterprise cares about runs on Spark inside AWS, SageMaker Unified Studio’s lineage view is genuinely sufficient. The moment a transformation happens in dbt on Snowflake or a notebook on Databricks, that view has a hole exactly where the data moved, and a cross-cloud layer with visibility into Databricks’ semantic layer for AI agents closes it.
When is AWS Glue Data Catalog alone genuinely enough?
Permalink to “When is AWS Glue Data Catalog alone genuinely enough?”If an entire data estate lives inside AWS, run by a small team with no immediate plan to operate AI agents across other platforms, Glue plus Lake Formation is usually enough, and buying a governance layer this early is over-engineering. A single-region shop running Athena over S3, with a handful of Redshift clusters and no active Snowflake or Databricks footprint, gets most of that value for a fraction of the setup cost. Teams weighing whether to build that layer themselves should read what a DIY context layer actually requires first.
The calculus changes once a second cloud enters the picture systematically, not as an experiment. That is usually the moment a team notices its glossary lives in disconnected wikis, its access policies have to be recreated by hand in a second AWS account, or its AI agent gives different answers depending on which system a user happened to ask about. Glue was never designed to solve that problem.
Working together: Glue as the AWS metastore, a governance layer across the estate
Permalink to “Working together: Glue as the AWS metastore, a governance layer across the estate”The two are not mutually exclusive, and most enterprises running both do not replace one with the other. Glue keeps serving as the fast, serverless metastore behind Athena, Redshift Spectrum, and EMR, with crawlers keeping schema current as S3 data changes. A cross-cloud governance layer, evaluated as an AI context platform rather than a Glue replacement, connects to Glue the same way it connects to every other source, layering business context, policy, and agent-facing access on top.
That tracks how enterprises actually evaluate this decision. Context layer evaluation criteria start with “does it replace what I have” and end with “does it unify what I have.” Teams working through how to implement an enterprise context layer for AI typically keep Glue as the AWS-native metastore and add the governance layer as the piece reaching across it, Snowflake, Databricks, and the parts of data infrastructure for AI that were never AWS-native.
The result, when it works, is that neither system does the other’s job. Glue stays fast and cheap for AWS query engines. The governance layer stays the one place an agent, a steward, or an auditor gets a governed, consistent answer, whether the table sits in S3 or a warehouse nobody has migrated off yet, and whether the policy comes from an AI governance framework or a plain classification rule.
Real stories from real customers: context across every catalog
Permalink to “Real stories from real customers: context across every catalog”"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."
— Joe DosSantos, VP Enterprise Data & Analytics, Workday
"Atlan is much more than a catalog of catalogs. It's more of a context operating system…Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models."
— Sridher Arumugham, Chief Data & Analytics Officer, DigiKey
Why the cloud boundary is the wrong place to stop governing data
Permalink to “Why the cloud boundary is the wrong place to stop governing data”AWS Glue Data Catalog earns its place in an AWS-native stack, and catalog federation makes it a genuinely better AWS-native tool than it was a year ago. Neither fact changes what Glue was scoped to do: catalog, and with Lake Formation, govern what lives inside AWS accounts, extended now to reading, not governing, a handful of Iceberg-compatible remote catalogs.
The enterprises hitting a wall are not the ones criticizing Glue. They are the ones whose context graph vs. knowledge graph question turned out smaller than their cross-cloud consistency question, and whose agents needed one governed answer to “what does this term mean,” not three depending on which cloud held the table. A cross-cloud governance layer does not compete with Glue for its AWS-native job. It picks up the job that starts the moment a second platform, and its agents, need to agree with the first.
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FAQs about AWS Glue Data Catalog and cross-cloud governance
Permalink to “FAQs about AWS Glue Data Catalog and cross-cloud governance”1. What is AWS Glue Data Catalog?
Permalink to “1. What is AWS Glue Data Catalog?”AWS Glue Data Catalog is AWS’s serverless, managed metadata repository for services like Athena, Redshift Spectrum, and EMR, and the technical metastore behind AWS Lake Formation.
2. Does AWS Glue Data Catalog work with non-AWS data sources?
Permalink to “2. Does AWS Glue Data Catalog work with non-AWS data sources?”In a limited way, through Glue connections and, since November 2025, catalog federation to remote Iceberg catalogs like Snowflake and Databricks Unity Catalog. Support is read-focused: query access, not shared governance.
3. Can AWS Glue Data Catalog govern data across multiple clouds?
Permalink to “3. Can AWS Glue Data Catalog govern data across multiple clouds?”Not on its own. Lake Formation permissions and LF-Tags apply within an AWS account and don’t propagate to consuming accounts, let alone to Snowflake or Databricks.
4. What changed with AWS Glue’s 2025 catalog federation?
Permalink to “4. What changed with AWS Glue’s 2025 catalog federation?”AWS query engines like Athena and Redshift can now read remote Iceberg tables in catalogs such as Snowflake Horizon and Databricks Unity Catalog without copying data. It federates query metadata and access, not glossaries or lineage.
5. What is a cross-cloud governance layer?
Permalink to “5. What is a cross-cloud governance layer?”A layer that unifies metadata from every platform an enterprise runs, not just one cloud, adding a business glossary, consistent policy enforcement, and governed context served to AI agents through an MCP server.
6. Does Atlan replace AWS Glue Data Catalog?
Permalink to “6. Does Atlan replace AWS Glue Data Catalog?”No. Atlan relies on Glue as the technical metastore for AWS-native services, adding the business glossary, cross-platform policy enforcement, and agent-facing context Glue doesn’t provide on its own.
7. When is AWS Glue Data Catalog alone genuinely enough?
Permalink to “7. When is AWS Glue Data Catalog alone genuinely enough?”If an estate lives entirely inside AWS, run by a small team with no immediate need for agents to reason across other platforms, Glue plus Lake Formation is usually sufficient.
8. Do AI agents need more context than AWS Glue Data Catalog provides?
Permalink to “8. Do AI agents need more context than AWS Glue Data Catalog provides?”Once an agent answers questions touching more than one platform, yes. Glue can tell an agent what a table in S3 looks like, but not whether that table’s definition of “active customer” matches the one used in Snowflake or Salesforce.
Sources
Permalink to “Sources”- AWS, “What Is AWS Lake Formation?” https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html
- AWS, “AWS Glue Data Catalog Announces Catalog Federation for Remote Apache Iceberg Catalogs” (November 2025). https://aws.amazon.com/about-aws/whats-new/2025/11/aws-glue-catalog-federation-remote-apache-iceberg-catalogs
- AWS, “Considerations and Limitations When Using AWS Glue Iceberg REST Catalog APIs.” https://docs.aws.amazon.com/glue/latest/dg/limitation-glue-iceberg-rest-api.html
- AWS, “Federate to Databricks Unity Catalog.” https://docs.aws.amazon.com/lake-formation/latest/dg/catalog-federation-databricks.html
- AWS, “Federate to Snowflake Iceberg Catalog.” https://docs.aws.amazon.com/lake-formation/latest/dg/catalog-federation-snowflake.html
- AWS, “Amazon SageMaker Unified Studio Supports Aggregated View of Data Lineage” (March 2026). https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-aggregated-lineage
- AWS, “Amazon SageMaker Now Supports Data Lineage in IAM-Based Domains” (July 2026). https://aws.amazon.com/about-aws/whats-new/2026/07/amazon-sagemaker-data-lineage-iam-domain/
- AWS, “Granting Cross-Account Access.” https://docs.aws.amazon.com/glue/latest/dg/cross-account-access.html
- Flexera, “2026 State of the Cloud Report.” https://www.flexera.com/blog/finops/flexera-2026-state-of-the-cloud-report-the-convergence-of-cloud-and-value/
- AI News, “IBM: Data Silos Are Holding Back Enterprise AI” (2026). https://www.artificialintelligence-news.com/news/ibm-data-silos-are-holding-back-enterprise-ai/
