Building vs. buying your data catalog

Building a data catalog used to sound relatively straightforward: collect metadata, index it, add search, and give users a place to find datasets.

That is no longer what organizations expect from a modern data catalog.

Today, a catalog may need to connect databases, cloud warehouses, transformation tools and BI platforms; capture lineage; manage business definitions and ownership; expose quality and trust signals; support governance workflows; and make context accessible to both people and AI systems.

The question is therefore no longer simply:

Should we build or buy a data catalog?

For most organizations, there are now three realistic paths:

1. Build a proprietary catalog internally.
2. Deploy and customize an open-source metadata platform.
3. Buy a managed data catalog or governance platform.

The right answer depends less on whether your engineering team can build a catalog and more on whether operating one is a strategic capability your organization wants to own for years.

What are you actually building?

Before comparing the options, define the requirement correctly.

A modern data catalog is not simply an inventory of tables. It connects technical metadata with the context people need to decide whether and how to use an asset.

That can include definitions, ownership, lineage, quality, classifications, policies, certification and relationships between datasets, dashboards, metrics and other assets.

A catalog therefore sits between several audiences.

Data engineers want reliable metadata ingestion and APIs. Data stewards need governance workflows and ownership. Analysts want to find trusted datasets quickly. Business users need definitions they can understand. Security and compliance teams need traceability. Increasingly, AI assistants and agents need governed context that can be consumed programmatically.

DataGalaxy’s guide to modern data catalog capabilities covers this broader evolution in more detail. Modern catalogs increasingly combine discovery, metadata, lineage, governance, collaboration and AI-oriented context rather than treating them as separate problems.

That changes the build-versus-buy calculation considerably.

Three approaches, not two

The traditional comparison between an internal build and a commercial platform misses the increasingly important middle ground: open source.

Approach	What you own	Main advantage	Main trade-off
Build from scratch	Product, architecture, integrations, infrastructure, UX and roadmap	Maximum control	Maximum engineering and maintenance responsibility
Open source	Deployment, configuration, much of the operations and customization	Control with an existing foundation	You still operate and evolve the platform
Buy	Governance operating model, configuration and adoption	Faster path to a supported product	Subscription cost and greater dependency on a vendor

None is automatically right or wrong.

The important distinction is where you want your organization to spend engineering capacity.

When building your own data catalog makes sense

Building internally gives you the highest level of control.

You decide the metadata model, architecture, APIs, search behavior, user experience and integrations. If the catalog itself is strategically important to your technical platform, that flexibility can matter.

There are organizations for which this makes sense.

A large technology company may have unusual metadata requirements, an established internal platform team and enough scale to justify dedicated engineers. Its internal catalog may also need to integrate with proprietary systems that commercial solutions were never designed to support.

But the decision should be based on more than the initial proof of concept.

A basic metadata search interface is relatively achievable. A production catalog is a continuously evolving product.

The real internal scope may include metadata ingestion, authentication, authorization, search, lineage, APIs, UI development, glossary capabilities, ownership, classification, observability, monitoring, upgrades, security fixes, documentation and support.

Every new source also creates an integration lifecycle.

If your organization adds a new warehouse, BI platform, orchestration tool or transformation technology, someone needs to build or adapt the connector and then maintain it as the underlying API changes.

The key question is therefore not:

Can our engineers build this?

It is:

Do we want a permanent team to own this product?

Open source changes the equation

Open source makes the decision more nuanced because organizations no longer have to start from zero.

Several mature projects provide substantial metadata and catalog capabilities.

The exact positioning differs, so they should not be treated as interchangeable.

Open-source project	Core orientation	What stands out	What to consider
OpenMetadata	Metadata platform and context layer	Catalog, lineage, governance, data quality, semantics and a broad connector ecosystem	You still need to deploy, operate, secure and upgrade the platform unless you choose a managed service
DataHub	Metadata and context platform	Discovery, lineage, governance, quality controls, APIs and extensibility	Self-hosting gives significant control but also infrastructure and operational responsibility
Amundsen	Data discovery	Strong focus on helping analysts and engineers search and discover data assets	More focused on discovery than an end-to-end governance operating model
Apache Atlas	Metadata and governance framework	Extensible type system, classification and governance, historically strong in Hadoop ecosystems	Often better viewed as foundational metadata infrastructure than a ready-to-adopt business-facing catalog

OpenMetadata currently describes itself as an open-source context and metadata layer covering cataloging, governance, quality, lineage and semantics, with more than 130 connectors. DataHub provides an Apache 2.0 self-hosted platform with discovery, governance and lineage and also offers a managed version. Amundsen remains a metadata-driven discovery engine, while Apache Atlas provides foundational metadata and governance services built around entities, classifications and APIs.

This makes open source attractive when the organization wants control over architecture and deployment without developing the metadata platform itself.

But open source does not mean zero cost.

The software license may be free. Operating the system is not.

Someone still needs to manage infrastructure, deployment, authentication, monitoring, upgrades, security, backups, connectors and customizations. DataHub’s own documentation, for example, distinguishes between its self-hosted Core product and Cloud specifically around managed infrastructure, enterprise support and operational capabilities.

For some engineering-led organizations, that trade is excellent.

For others, it simply changes the type of catalog they are building from “build the product” to “build the platform around the product.”

Compare total cost of ownership, not license price

One of the biggest mistakes in a build-versus-buy evaluation is comparing developer salaries with a software subscription.

The comparison needs to include the full lifecycle.

Cost area	Build	Open source	Managed platform
Initial engineering	Very high	Medium	Low
Infrastructure	Internal	Internal	Mostly vendor-managed
Connector development	Primarily internal	Mix of community and internal	Primarily vendor-managed
Upgrades	Internal	Internal	Vendor-managed
Security patches	Internal	Internal	Vendor-managed
Product roadmap	Internal	Community + internal	Vendor
UX development	Internal	Existing UI + customization	Vendor
Support	Internal	Community/internal or paid	Included according to contract
Training and adoption	Internal	Internal	Shared with vendor
Customization	Maximum	High	Product-dependent

The exact cost obviously depends on scale and architecture.

The useful exercise is to calculate the people required to operate the catalog after launch, not just to implement it.

Assume, for example, that an internal catalog requires only part of an engineer’s time after the initial project. What happens when the organization doubles its number of data sources? When an integration breaks? When users request lineage? When security standards change? When a new class of AI assets needs to be cataloged?

Catalogs have a habit of becoming more important after adoption, not less.

The maintenance curve matters.

Connectors are a product, not a one-time integration

Connectors deserve particular attention because they are easy to underestimate.

An enterprise catalog typically needs metadata from several classes of systems: databases and warehouses, transformation tools, orchestration systems, BI platforms, quality tools and sometimes operational applications.

Each integration has to extract the right metadata and preserve relationships between assets.

And the work does not end after the first successful connection.

Source APIs change. Authentication mechanisms evolve. New metadata types appear. Lineage logic improves. Customers request additional objects.

This is one reason established catalog platforms invest heavily in connector ecosystems.

DataGalaxy, for example, integrates catalog information with lineage, glossary definitions, ownership and governance context rather than treating ingestion as an isolated technical task.

When comparing build, open source and buy, count connector maintenance as an ongoing capability.

Do not underestimate business context

Technical metadata is only half the catalog.

Schemas can tell you that a column is called customer_status. They cannot tell you which definition of “active customer” the business has approved.

That context comes from people.

A modern catalog therefore needs to connect technical assets with a business glossary, ownership, policies and other governance information.

This is particularly important when the catalog expands beyond engineers.

An analyst may search for a table. A business user is more likely to search for “monthly recurring revenue” or “active customer.”

The platform needs to bridge those two languages.

The distinction between a catalog, glossary and data dictionary is explored further in DataGalaxy’s comparison of the three concepts.

This is also an important build-versus-buy question: who will design and maintain the experience for non-technical users?

A technically excellent metadata backend can still fail as a company-wide catalog if users cannot navigate it or understand what they find.

Lineage raises the technical bar

Lineage is another capability that looks simpler from a distance than it is in production.

At the basic level, lineage shows that one asset feeds another.

Useful lineage goes further. It may need to work across multiple technologies, identify transformations at column level and support impact analysis when something changes.

That means parsing SQL, understanding transformations and connecting metadata from multiple systems.

The result is valuable because lineage turns the catalog from an inventory into an operational tool.

If a source changes, teams can identify downstream dashboards or models. If a metric is wrong, they can trace it upstream. If an auditor asks where information came from, they can follow its path.

You can explore that use case in more detail in DataGalaxy’s guide to data lineage. DataGalaxy currently positions lineage together with ownership, quality context and impact analysis rather than as an isolated diagram.

If lineage is part of your requirements, include its implementation and maintenance in the decision from day one.

AI adds another requirement: machine-consumable context

The 2023 version of this build-versus-buy debate largely centered on human users.

That is changing.

AI assistants and agents are becoming another consumer of enterprise metadata. They need to understand which datasets are certified, what metrics mean, which assets are deprecated and what policies apply.

A catalog therefore increasingly serves two interfaces:

a human interface for discovery and collaboration, and a machine interface for governed context.

That raises new questions for an internal or open-source deployment.

Can AI applications access the context programmatically? Are permissions preserved? Are business definitions connected to technical metadata? Can an application distinguish certified assets from deprecated ones? Can lineage and ownership be retrieved without scraping a UI?

This does not mean every organization needs an “AI catalog.”

It means the architecture chosen today should not assume that humans will always be the only consumers of metadata.

Adoption is part of the product

There is one cost category engineering estimates regularly miss: adoption.

A catalog only creates value when people use it.

That requires more than uptime.

Users need search that works, understandable terminology, clear ownership, intuitive navigation and trust signals that answer practical questions.

The governance team also needs ways to review definitions, certify assets, coordinate stewards and keep knowledge current.

A home-grown catalog can absolutely provide these capabilities.

The question is whether your organization wants to build them.

For a useful overview of what adoption requires beyond the technical platform, see DataGalaxy’s guide to organizing data with a modern data catalog.

A practical decision framework

The choice becomes clearer when you score the three approaches against your actual constraints.

If this describes your organization…	Approach worth exploring first
Metadata infrastructure is a strategic engineering capability	Build
You have a strong platform team and require architectural control	Build or open source
You want source-code access and self-hosting but not a greenfield project	Open source
Your team is comfortable operating Kubernetes, integrations and metadata services	Open source
Business adoption and governance workflows are immediate priorities	Buy
You need to deploy across many systems quickly	Buy or mature open source
You have limited engineering capacity for catalog operations	Buy
Enterprise support and predictable operational responsibility matter	Buy or managed open source
Your requirements are highly proprietary and unlikely to exist in standard products	Build

The table is deliberately not a verdict.

A financial institution with strict hosting requirements may select open source because it wants complete infrastructure control.

A technology company may build because metadata is part of its core platform.

A large enterprise may buy because it wants data engineers working on products rather than maintaining governance infrastructure.

All three decisions can be rational.

Seven questions to answer before deciding

Before committing, force the team to answer a few uncomfortable questions.

What is our actual scope? Discovery only, or glossary, lineage, governance, quality context, workflows and AI consumption too?

Who owns the platform after implementation? Name the team, not the department.

How many integrations will we need in two years? Use the future architecture, not today’s stack.

Who maintains those integrations? Include version changes and failures.

Who owns the user experience? Especially for analysts, stewards and business users.

How will we measure adoption? A technically successful catalog with no users is still a failed catalog.

What happens if our requirements change? Consider new regulations, acquisitions, AI initiatives and new data platforms.

The quality of those answers is usually more useful than a generic build-versus-buy scorecard.

When buying becomes the pragmatic choice

Buying tends to make sense when the organization considers cataloging and governance essential but does not consider building metadata infrastructure a competitive advantage.

The trade is straightforward.

Instead of funding the catalog as an internal software product, the organization pays a vendor to maintain the underlying product, integrations, infrastructure and roadmap while its own teams focus on governance, adoption and business outcomes.

A modern platform such as DataGalaxy Catalog brings together cataloging, business definitions, lineage and governance rather than requiring teams to assemble each capability separately.

That does not eliminate internal work.

You still need owners. You still need stewards. Definitions still require business decisions. Governance still needs an operating model.

Buying software removes product engineering work. It does not outsource accountability.

That distinction is important.

When open source is the better compromise

Open source can be the strongest choice when the organization sits between the two extremes.

You may want control over infrastructure, metadata models and extensibility without investing years in a proprietary platform.

In that case, projects such as DataHub or OpenMetadata provide a much stronger starting point than a blank repository.

But approach the decision as adopting a platform, not downloading a free tool.

Prototype the deployment. Test your most difficult connectors. Validate authentication and permissions. Measure upgrade effort. Confirm the skills needed to operate it. Ask who handles incidents on Monday morning.

If those answers fit your engineering operating model, open source can be highly effective.

If they do not, a managed platform may be cheaper even with a higher visible license cost.

The real build-versus-buy question

There is no longer a credible universal rule that every organization should buy a data catalog.

Nor is open source automatically the inexpensive option.

And the existence of talented engineers is not, by itself, a reason to build.

The decision comes down to ownership.

If metadata infrastructure is strategic enough that your organization wants to design, operate and evolve it as an internal product, building may be justified.

If you want control and extensibility while starting from a mature foundation, open source deserves serious consideration.

If the priority is to make trusted data discoverable and governed without creating another internal software product to maintain, buying is usually the more direct path.

The question to ask is not:

“How cheaply can we get a catalog running?”

It is:

“Which parts of the data catalog do we want to own three years from now?”

That is the comparison that reveals the real cost.

Key takeaways

• The decision is no longer simply build versus buy. Mature open-source metadata platforms create a credible third path.
• Compare operating models, not just upfront cost. Infrastructure, connectors, upgrades, security, UX and support continue long after launch.
• A modern catalog is more than metadata search. Business context, lineage, governance, trust signals and machine-consumable context increasingly matter.
• Open source reduces product-development work, not operational responsibility.
• Buying software does not outsource governance. Owners, stewards and business definitions still belong to the organization.
• Build when metadata infrastructure itself is strategic. Buy or adopt open source when your competitive advantage lies elsewhere.
• Measure adoption. A catalog that is technically complete but rarely used is simply an expensive inventory.

About the author: Jessica Sandifer

Tech writer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.

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