A data catalog is the internal inventory a data team uses to find, trust, and govern its own data assets. A data marketplace is the self-service storefront everyone else, increasingly including AI agents, uses to discover and access data products built on that catalog. According to Gartner, 26% of organizations have already implemented a data marketplace, with 31% more planning to by 2027. That single word, marketplace, covers two different things: external exchanges like Snowflake Marketplace, and internal governed storefronts.
Both terms get used loosely, and most search results repeat the same surface-level split. What gets missed: audience, not internal versus external, is the sharper way to tell a catalog from a marketplace, and “data marketplace” itself splits into two genuinely different products depending on who is buying.
- Audience is the real differentiator. A catalog serves the data team; a marketplace serves the rest of the organization, plus any AI agent that needs a governed answer.
- “Data marketplace” means two things. An external commercial exchange, such as Snowflake Marketplace, AWS Data Exchange, or Databricks Marketplace, or an internal governed storefront built on a company’s own catalog.
- Neither replaces the other. A marketplace with no catalog underneath has nothing trustworthy to sell; a catalog with no marketplace on top never reaches anyone outside the data team.
- AI agents are a growing third audience, consuming data products through MCP and other governed interfaces alongside human buyers.
Below: what each one is, how they compare head-to-head, how they work together, and how Atlan approaches both.
| Dimension | Data marketplace | Data catalog |
|---|---|---|
| What it is | A self-service storefront for discovering and accessing data products | An inventory and system of record for an organization’s data assets |
| What it does | Surfaces certified, ready-to-use data products | Documents, classifies, and governs raw data assets |
| Primary audience | The whole organization, plus AI agents | The data team: engineers, analysts, stewards |
| Unit of exchange | Data products, or external datasets for commercial exchanges | Metadata records for tables, columns, and dashboards |
| Key strength | Discoverability and self-service adoption | Lineage, ownership, and governance depth |
| Best for | Getting non-technical users into the data estate | Inventorying data that has never been catalogued |
| Complexity level | Lower for end users, higher to govern well | Higher to build, lower to consume once built |
Data marketplace vs data catalog: what’s the difference?
A data catalog and a data marketplace solve different problems for different audiences, and much of the confusion comes from “data marketplace” quietly meaning two different things. A catalog is a management tool built for the data team: it inventories tables, columns, and dashboards so engineers and stewards can find, document, and govern them. A marketplace is a self-service storefront built for everyone else, including AI agents that now query data estates directly. Audience, not internal versus external, is the split that actually holds up: a catalog manages, a marketplace serves.
That split is also where the market is moving, not just where it started. Two established data catalog vendors shipped catalog-attached, marketplace-style access features within the same quarter in 2026, a signal that governed self-service on top of an existing catalog is becoming table stakes rather than a niche add-on. According to Gartner (cited via Huwise, 2026), data and analytics leaders who invest in a data marketplace are projected to see over four times greater adoption of their data products by the business by 2028, a scale of cross-team adoption a catalog on its own is not designed to produce.
Most of the remaining confusion traces to a single word doing two jobs. External marketplaces let organizations buy and sell third-party datasets; internal storefronts let the rest of the company, not outside buyers, discover and access data it already owns. Multiple general glossaries, including Denodo’s, describe the external, commercial meaning first, part of why the internal, governed meaning gets overlooked. Two adjacent terms add to the confusion without being either one: the dbt Semantic Layer defines what a metric means, not where data lives, and is a dbt Cloud Starter or Enterprise feature according to dbt Labs. A data mesh decentralizes who owns data, not how people find it, and a marketplace can sit on either a mesh or a centralized catalog. Context layer, data catalog, and semantic layer, and semantic layer and data catalog, split apart along the same audience line as marketplace and catalog.
Resolving which marketplace is meant, and which audience is being built for, matters more than picking a vendor. Get the audience wrong and neither a catalog nor a marketplace earns adoption, no matter how complete the metadata underneath it is.
What is a data catalog?
A data catalog is the centralized inventory of an organization’s data assets and the metadata that makes them findable, trustworthy, and governable. It exists primarily for the data team: engineers who need to know where a table came from, analysts who need to know whether a metric is certified, and stewards who need to enforce a policy before an asset ships. It does not sell or broker anything; it documents and governs what already exists, which also distinguishes it from adjacent categories, such as how a data catalog differs from master data management.
Catalogs used to be graded on how completely they inventoried an estate: assets documented, terms defined, seats onboarded. That bar is moving, because the same shift that took metadata supply from manual curation toward AI-assisted, evidence-generated context is now reshaping who has to be served by what the catalog produces, not just how much of it exists.
Metadata supply has moved from manual curation, to automated suggestions, toward autonomous generation. Metadata consumption has moved in parallel, from siloed access, to collaboration embedded in daily tools, toward conversational retrieval an AI agent can call directly. A catalog built only for the first stage of either shift will not serve the audience a marketplace is built for.
Core components of a data catalog
- Technical metadata: data types, size, and storage location
- Operational metadata: update frequency and lineage that traces where a number actually came from
- Business metadata: definitions, purpose, and ownership, often modeled as a knowledge graph rather than isolated records
- Governance and access controls: policy enforcement that determines who, or which agent, can act on an asset
- Search and discovery interface: how a person, or an AI agent treating the catalog as a knowledge base, actually finds what they need
None of this changes what a catalog is for: proving an asset is trustworthy before anyone, human or agent, is allowed to act on it. What changes is who is waiting on the other side of that proof.
What is a data marketplace?
“Data marketplace” describes two genuinely different products. One is an external, commercial data exchange for buying and selling third-party datasets. The other is an internal, governed data-product storefront built on top of an organization’s own catalog, for its own people and its own AI agents. General definitions like IBM’s describe the external, commercial version by default, which is not what most enterprise buyers evaluating “data marketplace software” actually mean.
According to Gartner’s 2024 Evolution of Data Management Survey, organizations that adopt a data marketplace are 1.9 times more likely to have data management functions that successfully deliver business value than those that do not. That correlation doesn’t distinguish which kind of marketplace respondents meant, and likely spans both, a sign that the underlying discipline (trustworthy, discoverable data products) matters more than which flavor an organization runs.
The center of gravity is shifting toward the internal, governed meaning: established catalog vendors are increasingly bundling marketplace-style, governed-access features onto their existing catalogs, evidence the internal meaning is becoming the category’s default, not a niche interpretation.
External and commercial data-exchange marketplaces
Three products define this category today, named here as factual examples rather than a ranked comparison, since none directly competes with a governed internal storefront:
- Snowflake Marketplace supports paid listings, so providers can charge for their data products, and operates across AWS, Google Cloud, and Azure, with Azure Government listed as planned support rather than general availability, according to Snowflake’s own documentation.
- AWS Data Exchange’s catalog includes both commercial and free or open data products, sourced from academic institutions, government entities, research institutions, and private companies, according to AWS’s documentation.
- Databricks Marketplace lists datasets, AI models, notebooks, and apps, and now centralizes MCP servers so AI agents can discover them, authenticated through Unity Catalog, according to Databricks and its own product documentation.
Cloud platforms increasingly bundle a catalog and a marketplace together, too: AWS pairs DataZone with Glue Data Catalog and separately markets DataZone as an enterprise data catalog in its own right, while Amazon Quick Suite gets evaluated against a governed catalog rather than a marketplace, and Google’s Knowledge Catalog gets positioned against a neutral catalog on BigQuery. None of this erases the distinction; it confirms cloud vendors hit the same two-layer problem.
Internal governed data-product storefronts
The internal version sits on top of a company’s own catalog, making the assets that catalog already governs findable and requestable by people who never learn its conventions: a marketplace and a catalog work as two required layers, not competing products, since the catalog inventories and classifies while the marketplace handles discovery and self-service access. Data products, tables, dashboards, models, and metrics published once with trust signals, ownership, and lineage attached, are the unit of exchange here, not raw datasets. Atlan’s own Data Marketplace is built this way: audience, again, is the differentiator, since the same metadata now has to reach a business analyst asking a plain-English question and an agent calling an MCP server for the same answer.
Whichever version an organization means, both ultimately depend on the same governance signals, lineage, ownership, and certification, to be trustworthy at all. A marketplace without that foundation is just an unverified directory with a nicer interface.
Data marketplace vs data catalog: head-to-head comparison
Once it is clear which kind of marketplace is meant, the catalog and the marketplace diverge sharply on audience, unit of exchange, and what “trustworthy” actually requires.
| Dimension | Data marketplace | Data catalog |
|---|---|---|
| Primary focus | Discovery, access, and exchange of data products | Inventory, understanding, and governance of data assets |
| Primary audience | Whole organization plus AI agents | Data team: analysts, engineers, stewards |
| Unit of exchange | Data products, or external datasets for commercial exchanges | Metadata records for tables, columns, and dashboards |
| Typical owner | Data platform or governance lead, business-facing | Data engineering or data governance team |
| Governance model | Policy enforced at the point of access and discovery | Policy defined and tracked at the source |
| Time to value | Fast for end users, once the catalog exists underneath | Slower; the foundational work happens here first |
| Tooling requirements | Self-service UI, conversational search, access workflows | Metadata scanners, lineage tooling, glossary |
| Organizational impact | Adoption across the whole company, not just data teams | Trust and consistency within the data team |
| Failure mode | A storefront nobody trusts because nothing underneath is governed | A catalog nobody outside the data team ever opens |
| Where AI agents fit | Consume certified data products directly via MCP or APIs | Supply the lineage and ownership signals agents need to trust an answer |
Consider a business analyst who asks an AI agent, “Can I use the Q4 revenue numbers in my board deck?” Answering well needs both layers. The marketplace has to surface a certified revenue data product with one-click access, so the analyst, or the agent acting for them, does not have to guess which of a dozen “revenue” tables is the real one. The catalog has to have already established that product’s lineage, ownership, and freshness, so the certification the marketplace displays is earned rather than asserted. Neither layer alone answers the question; treating the catalog as the knowledge base an agent actually reads from only works if the marketplace packages that knowledge into something an agent can act on.
These dimensions are not really about which platform to buy. They are about which failure mode an organization is exposed to right now: a marketplace nobody trusts, or a catalog nobody outside the data team ever opens.
How do data marketplaces and data catalogs work together?
A marketplace without a catalog underneath has nothing trustworthy to sell; a catalog without a marketplace on top never reaches anyone outside the data team.
Certification and trust signals flow from catalog to marketplace
The catalog’s lineage, ownership, and quality metadata becomes the trust signal a marketplace listing displays: the catalog supplies lineage and freshness, the marketplace supplies discovery and the access mechanism, both resting on the same core components of a context layer. Combined, a non-technical user, or an agent acting on their behalf, can trust a data product without asking the data team to vouch for it first.
Policy enforcement at the point of discovery
Policies defined once in the catalog get enforced automatically the moment someone requests access through the marketplace: Immuta, for example, enforces masking and column-level restrictions in Snowflake or Databricks once a request is approved. The catalog supplies the classification; the marketplace supplies the request and approval workflow. Getting this wrong is usually a data governance taxonomy problem before it’s a marketplace problem, worth checking against why governance implementations fail elsewhere before blaming the interface. Done well, no manual ticket queue sits between a request and an answer.
AI agents as a third audience, not just people
MCP exposes a marketplace’s data products, and the catalog’s lineage and ownership behind them, to any agent that supports the protocol, so agents and humans query the same governed layer rather than two separate ones. The catalog supplies the governed metadata; the marketplace supplies the packaged, certified product, with agent access control deciding what either can see. The result, when lineage retrieval is built for how agents actually query, is one shared layer of context for both.
When to prioritize one over the other
Start with a catalog when no one has inventoried what data exists yet, or the data team itself can’t reliably find or trust its own assets. Start with a marketplace layer when a catalog exists but only the data team ever opens it, and adoption outside that team is the real problem. Invest in both at once on a greenfield build, or when standing up AI-agent access to a data estate for the first time. A build-versus-buy evaluation is worth running first, since the same evaluation criteria apply to either gap, and a team weighing a do-it-yourself approach should size that work honestly before starting.
Treating catalog and marketplace investment as a sequence rather than a rivalry is what actually gets both built. The order depends on which audience is currently unserved, not on which category a vendor happens to sell.
How Atlan approaches data marketplace and data catalog
Most organizations either bolt a marketplace feature onto an aging catalog, or buy a commercial data exchange that has nothing to do with their own data. Atlan treats both as one product.
Siloed catalogs that only the data team opens, and commercial marketplaces that solve data acquisition but say nothing about internal trust, are two separate gaps most organizations live with at once. Vendors bundling governed-access features onto existing catalogs confirms the gap is real, usually as a marketplace feature bolted onto a catalog that was never built to be the trust layer underneath it.
Atlan’s Data Marketplace includes the catalog layer underneath instead of selling it separately. Data products, tables, dashboards, models, and metrics packaged with built-in trust signals, ownership, and lineage, are the unit of exchange, with policies attached directly to assets and enforced automatically through integrations like Immuta in Snowflake or Databricks. An MCP server exposes that same lineage, ownership, classification, and policy layer to Claude, Copilot, and ChatGPT, so an engineer troubleshooting a broken dashboard and a business user checking whether a number is safe to use draw on the same governed context, not two disconnected systems. The same shift that moved catalogs from manual curation toward AI-assisted context is reshaping who a marketplace serves: data teams first, the rest of the organization next, AI agents now too. It’s also reshaping how either layer gets measured: coverage, assets documented, terms defined, seats onboarded, was always a proxy. A catalog-plus-marketplace built as one system has to be judged on whether an agent or user got the right answer, not on how much of the estate carries a description.
Kiran Panja, MD, Cloud & Data Engineering, CME Group, described the effect directly: “The UI was so intuitive that even first-time users could search, navigate and find what they needed. Within the first year after that we cataloged over 18 million assets, defined more than 1,300 glossary terms, and we are tackling new use cases every quarter.” That’s what a catalog and marketplace working as one system produce: adoption outside the data team, without giving up what made the catalog trustworthy in the first place.
For teams weighing whether to unify the two, the enterprise context layer is the underlying architecture question, why AI agents need one covers the case, and a practical implementation path exists once that’s decided. A catalog and marketplace still built as separate systems solve only half of either problem, and context layer ROI covers what closing that gap is worth.
Real stories from real customers: audience, not just accuracy
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."
— Andrew Reiskind, Chief Data Officer, Mastercard
"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 and Analytics, Workday
Why the catalog underneath is what makes a marketplace trustworthy
The marketplace-versus-catalog question was never really “which one do we need.” It is “which audience, and which meaning of marketplace, are we even talking about.” Once that is answered, the two stop looking like alternatives and start looking like layers in the same stack: whichever kind of marketplace an organization runs, external or internal, what makes it trustworthy is the same thing a catalog produces, lineage, ownership, and certification. Treating catalog and marketplace as rival budget lines misreads that relationship. As AI agents become a marketplace’s third audience alongside data teams and the rest of the organization, the context layer underneath both is what has to hold, not the storefront built on top of it.
FAQs about data marketplace vs data catalog
1. What is the difference between a data catalog and a data marketplace?
A data catalog is a management tool built for the data team to inventory, document, and govern data assets. A data marketplace is a self-service storefront built for the rest of the organization, and increasingly AI agents, to discover and access data products. Audience is the real differentiator, not internal versus external.
2. What is a data marketplace?
A data marketplace is a platform for discovering and accessing data, and the term covers two different products. One is an external commercial exchange for buying and selling third-party datasets. The other is an internal, governed storefront built on top of an organization’s own catalog for its own people and agents.
3. Is a data marketplace the same thing as a data mesh?
No. A data mesh is an organizational and architectural pattern that decentralizes data ownership to domain teams. A data marketplace is a discovery and access layer that can sit on top of either a centralized catalog or a decentralized mesh. One describes who owns the data; the other describes how people find it.
4. Do you need both a data catalog and a data marketplace?
Most organizations eventually need both, but not necessarily on day one. A catalog comes first if data is not yet inventoried or trusted internally. A marketplace layer is worth adding once a catalog exists but adoption outside the data team has stalled.
5. What are the best data marketplace platforms?
Snowflake Marketplace, AWS Data Exchange, and Databricks Marketplace are the established platforms for buying and selling third-party datasets, each tied closely to its own cloud ecosystem. None of them compete with an internal governed data-product storefront, since they solve data acquisition rather than internal discovery and trust.
6. How do you build an internal data marketplace?
Start from an existing catalog’s metadata rather than from scratch. Package the assets that matter most as certified data products with ownership and lineage attached, then layer self-service discovery and access requests on top, enforced by policy rather than a manual approval queue.
7. What are the risks of a data marketplace?
The recurring risks are trust, pricing or valuation, and access-approval bottlenecks. A marketplace nobody trusts gets ignored regardless of how polished its interface is, and one with no automated policy enforcement behind it just moves the ticket queue somewhere more visible.
8. Can AI agents use a data marketplace directly?
Yes, through a governed interface such as MCP. An agent can query a marketplace’s certified data products and the catalog’s lineage and ownership signals behind them, the same way a human user would, without needing separate tooling built specifically for machine access.
Sources
- Data Marketplaces Are Key to Faster Data Product Adoption and AI-Ready Data Delivery, Gartner via Huwise
- Snowflake Marketplace and Listings, Snowflake
- What Is AWS Data Exchange?, AWS
- What is Databricks Marketplace?, Databricks
- MCP Marketplace Brings Real-Time Intelligence to Agentic Applications, Databricks
- dbt Semantic Layer, dbt Labs
- What is a data marketplace?, IBM
- Data Marketplace, Denodo
