Data Marketplace vs Data Catalog: What’s the Difference?

You can have a best-in-class data catalog and still hear the same complaint from the business: “Great, we found it. Now, how do we actually use it?” That disconnect is why “data catalog” and “data marketplace” should not be treated as interchangeable terms. They solve different stages of the data lifecycle. A data catalog is built for discovery and understanding: What exists, where it lives, what it means, and who owns it. A data product marketplace is built for consumption and sharing, as it turns raw assets into governed products people can request, access, trust, and reuse. This post explains the difference, shows where each one stops, and helps you decide when you need both. If you want the foundational definition and examples of marketplaces, start with our pillar page “What is a Data Marketplace?”, then come back for the “vs” breakdown below.

Key takeaways

• A data catalog helps people find and understand data assets; a data product marketplace enables them to access, trust, and reuse governed data products.

• If your pain starts after discovery (access delays, conflicting KPIs, repeated extracts), it’s likely that a data catalog is no longer enough for you.

• A combination of a data catalog for metadata backbone and a data product marketplace for product packaging, streamlined access workflows, and visible trust signals.

• Peaka helps teams publish governed, permissioned data products so consumption is self-serve.

The problem: Finding data

Most organizations think their problem is “we can’t find data.” Often, the real problem is consuming data safely and consistently. Discovery is only the first step. After someone finds a dataset or dashboard, they still need answers to operational questions: Who can approve access? Which version is correct? Is this metric certified? How fresh is it? What happens if the schema changes next week?

Without a clear consumption path, teams fall back to workarounds. They export CSVs, copy tables into personal workspaces, or rebuild logic in spreadsheets. In addition to being inefficient, these methods also break governance. Policies may exist, but bypassing them for speed becomes tempting, leading to broken governance.

This is the gap between metadata awareness and usable value. Catalogs excel at the first part, but marketplaces are essential for the second. They facilitate packaging, access workflows, and trust signals that make compliant usage the norm.

Data catalog: What it does

A data catalog is a system of record for metadata. It helps users discover assets across warehouses, lakes, BI tools, and pipelines, then understand what those assets mean. At its best, a catalog connects technical context (lineage, schemas, upstream sources) with business context (glossary terms, owners, descriptions, classifications). This is essential infrastructure. It reduces duplicate effort, improves documentation, and enables effective data stewardship at scale.

A catalog typically stops when a user attempts to take action. Catalogs rarely offer a complete workflow from discovering an asset to being able to use it. Access is often handled elsewhere through separate processes like tickets, manual approvals, or tribal knowledge. Trust is implied by documentation, not proven at the point of consumption. Even “certified” badges can become outdated if they are not backed by checks and operational ownership.

Therefore, the issue isn’t that catalogs are ineffective; rather, they are designed to excel at finding and understanding assets rather than delivering governed consumption as a workflow.

Data product marketplace: What it changes

A data product marketplace transforms discovery into tangible results by treating data as a consumable end-to-end asset, not merely something to locate. The fundamental shift lies in the unit of value: moving from “a table" to a data product. Such a product is designed for reuse, featuring a clear purpose, defined interfaces (such as tables, APIs, and semantic models), version control, an owner, and quality and update standards. This productization is what makes consumption repeatable.

Next is access. A marketplace facilitates practical, governed access, where users can request access (or be auto-provisioned based on policy), approvals are captured in a workflow, entitlements are applied where data resides, and audits become straightforward. The experience is closer to “self-serve with guardrails” than “submit a ticket and hope for the best.”

Finally, marketplaces display trust signals at the decision point, such as freshness, quality checks, certification, ownership, SLAs, and usage context. This reduces debate and rework, guiding teams toward trusted products instead of quick, less reliable workarounds.

Data catalog vs data product marketplace

The simplest way to differentiate is this: Catalogs specify “what exists,” whereas marketplaces focus on “how to use it safely.” Both are important, but they have distinct objectives and success measures. A catalog acts as a map, while a marketplace includes the store, checkout process, policies, and receipts.

Here’s a practical comparison you can use when explaining the distinction internally:

If search and documentation are your main challenges, begin with a catalog. If issues revolve around access, trust, and reuse, you're already operating within marketplace territory.

When you need both

In mature stacks, the best answer is not “catalog or marketplace.” Instead, both should be integrated seamlessly. Think of the catalog as your metadata backbone and the marketplace as your consumption layer. The catalog provides lineage, glossary alignment, technical discovery, and classifications. The marketplace builds on that foundation to publish products, enforce access workflows, and present trust signals in a user-facing way.

A healthy integration typically involves the marketplace retrieving technical metadata from underlying sources, often via the catalog’s metadata graph, and then generating product pages that link back to lineage and definitions. Certification in the marketplace should map to real checks and accountable owners, not static labels. Access decisions must translate into entitlements at the point of storage or delivery, ensuring governance is enforced where it matters most. Quality and freshness signals should update automatically, so trust does not rely on manual upkeep.

If you already have a catalog, the fastest path is to productize the top 10–20 most requested assets and publish them through a marketplace workflow.

Which one do you need?

If a data catalog is working well, you will see faster discovery, clearer ownership, and fewer “unknown” assets. If you have outgrown a catalog-only approach, the pain shows up after discovery. Use the checklist below to self-diagnose. Checking five or more signs typically indicates that adding a marketplace layer is the next logical step.

Access and workflow

• Access requests take more than 48 hours on average

• Users find assets, but do not know how to get permission

• Approvals and audits are manual, inconsistent, or scattered

Trust and consistency

• The same KPI has multiple definitions across teams

• “Certified” data is not obvious at the point of use

• Data freshness and quality are not visible without asking someone

Reuse and scalability

• The same extracts are recreated every month

• Teams build shadow pipelines or spreadsheets to move faster

• Ownership is unclear, or the owners cannot support consumers

A data catalog improves visibility. A data marketplace improves the reliability of consumption. When trust and access become bottlenecks, you've crossed the point of optimal system design.

Common misconceptions

“A marketplace is just a prettier catalog.”

A better interface helps, but it is not the point. The difference is operational: Marketplaces showcase products, enforce access workflows, and highlight trust signals that influence real usage.

“We can do this with tickets and policies.”

You can, at small scale. But tickets do not scale with demand, and policies do not help if they are not embedded in the workflow. When speed is prioritized, governance often gets neglected.

“Marketplaces are only for external monetization.”

External sharing is one use case, but internal value is often greater and faster, as it provides fewer duplicated pipelines, faster analytics, and consistent, certified metrics across teams.

“If we have a marketplace, we do not need a catalog.”

Most organizations still benefit from strong catalog capabilities for lineage and glossary management. The marketplace serves as the primary access point for consumption, not a substitute for metadata discipline.

If any of these misconceptions are driving your tool decisions, you may be solving the wrong problem with the wrong layer.

Practical next steps

Start with a narrow, high-impact scope. Pick one domain where inconsistency and access delays cause major business pain: Finance metrics, customer 360, risk reporting, claims, or revenue operations. Target a single audience (analysts, product teams, compliance, or leadership) so you can measure outcomes quickly.

Next, define lightweight product standards for your first set of data products:

• owner and escalation path

• freshness standards and update schedule

• automatic quality checks

• certification criteria and review cycle

• versioning and change communication

• access policy (auto-provision vs approval workflow)

Then publish your first products through a marketplace-style experience featuring clear product pages, request or grant access, and trust signals that update automatically. Treat every access request and consumer question as product feedback, not as a one-off ticket.

Conclusion

In the last few years, we’ve seen a flood of new terms emerge, such as data mesh, data fabric, logical data warehouse, semantic layers, data observability, data contracts, and now data marketplaces. That’s not just hype. It’s a response to a real shift.

Data has moved from a back-office byproduct to a shared, operational asset that more teams depend on, more often, with higher expectations for speed, trust, and accountability. In this new era, data catalogs and data product marketplaces solve real problems for modern enterprises. While the former helps users locate data, the latter turns datasets into governed products, streamlining access and making trust visible at the point of consumption.

If you’re ready to move from “we found it” to “we can use it safely,” Peaka is built for exactly that shift. It helps you turn datasets into searchable data products with shared definitions and granular access controls, so teams can consistently use and securely share governed data end to end.

Book a demo to see how a data product marketplace can sit on top of your existing stack, adding permission-based discovery, product-level governance, and an AI-ready layer without disrupting current workflows.