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Compare 17 data catalog tools for 2026, with pricing, strengths and trade offs for each. See which data catalog software fits your stack.
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A data catalog is the inventory of every data asset an organisation holds, together with the metadata that makes each one usable: where it came from, who owns it, what it means, how fresh it is and who is allowed to see it. Without one, the same question gets answered three different ways by three different teams, each using a table they found on their own.
That is the short version, and it is all this article needs. If you want the longer explanation, including how catalogs changed once AI systems started reading from them, our guide to what an AI data catalog is covers it properly, and the primer on data catalog concepts covers the underlying metadata model. This page is about choosing between the products.
One distinction is worth setting up before the list, because buyers routinely conflate four categories that solve different problems.
| Category | The question it answers | What it does not do |
|---|---|---|
| Data catalog | What data do we have, what does it mean, and where did it come from? | It does not continuously test whether the data is correct. |
| Data observability | Is the data correct, fresh and complete right now? | It does not maintain a business glossary or an ownership register. |
| Data governance platform | Who is accountable, what are the policies, and can we prove they held? | It usually depends on a catalog underneath it for the asset inventory. |
| Master data management | What is the single correct record for this customer or product? | It covers a narrow slice of entities, not the whole estate. |
Most of the products below have absorbed at least one neighbouring category. That is why shortlists get confusing, and why the comparison table matters more than the marketing page. Our explainer on data observability sets out where the boundary sits if your requirement spans both.
This is the fast version. Every tool in it is covered in detail further down, with strengths, trade offs and a pricing position.
| Tool | Best for | Deployment | Pricing |
|---|---|---|---|
| Decube | Regulated data teams that must evidence lineage and control | Cloud, or inside the customer environment | Published, from 21,000 USD a year |
| Alation | Large enterprises wanting adoption through search and stewardship | Cloud and self managed | Not published, enterprise agreement |
| Atlan | Modern data teams that want fast adoption and collaboration | Cloud | Not published, enterprise agreement |
| Collibra | Enterprises where governance workflow is the driver | Cloud | Not published, among the most expensive in the category |
| Informatica CDGC | Complex estates with heavy integration and classification needs | Cloud | Not published, consumption units |
| Microsoft Purview | Organisations standardised on Azure and Microsoft 365 | Cloud | Published, consumption based with some per user capabilities |
| Databricks Unity Catalog | Databricks first teams | Inside Databricks | Published, included in Databricks consumption |
| Snowflake Horizon Catalog | Snowflake first teams | Inside Snowflake | Published, included in Snowflake consumption |
| AWS Glue Data Catalog | AWS estates needing a technical metastore | Inside AWS | Published, consumption based per object stored and per request |
| dbt | Teams whose definitions already live in dbt models | Cloud and open source core | Published, per developer seat with a free tier |
| OpenMetadata | Engineering led teams wanting a full catalog without licence fees | Self hosted or managed | Free and open source, managed offering not published |
| DataHub | Large engineering organisations wanting an extensible metadata graph | Self hosted or managed | Free and open source, managed offering not published |
| Amundsen | Teams that mainly need search and discovery | Self hosted | Free and open source |
| Secoda | Small and mid sized data teams wanting speed over depth | Cloud | Published, per user tiers |
| data.world | Knowledge graph driven governance and data sharing | Cloud | Published tiers plus enterprise quotes |
| OvalEdge | Mid market teams wanting a broad suite at a lower entry point | Cloud and self managed | Not published, quoted by module and user |
| CastorDoc | Analytics teams wanting documentation to happen automatically | Cloud | Not published |
Every vendor claims all of the following. The useful question is not whether a feature exists but how much manual work it removes, so each item below is written as the test to run in the demo rather than as a box to tick.
| Capability | The test to run in the demo | What a weak answer sounds like |
|---|---|---|
| Metadata harvesting | Ask the vendor to connect one of your less common sources during the evaluation, not after the contract. | "That is on the roadmap" or "we would build a custom connector for you". |
| Column level lineage | Pick one number on a live dashboard and ask them to trace it to source columns, on your data. | A prepared demonstration environment, or lineage a consultant maintains by hand. |
| Business search | Type a business term rather than a table name and see what comes back. | "Your stewards would tag that during onboarding". |
| Access enforcement | Ask whether the tool grants access itself or pushes policy to the warehouse, and see the mechanism. | "We record the approved policy" with no enforcement path at all. |
| Audit history | Ask for a point in time export showing who could see a given table eighteen months ago. | A current state dashboard offered as though it answered the question. |
| Metadata portability | Ask how metadata leaves the product, and request a sample export. | An API described but never demonstrated. |
The two that separate products most in practice are computed column level lineage and exportable audit history, because they are expensive to build and cheap to claim. They are also the two that a governance programme is judged on. Our overview of data governance concepts sets out how those capabilities map onto the wider programme.
Decube appears first because this is the Decube blog and pretending otherwise would insult the reader. Everything after that is ordered by category, starting with the enterprise platforms, then the platform native catalogs, then the open source projects, then the challengers. Each entry states where the product is genuinely better than Decube, because a comparison that never concedes a point is not useful to a buyer and will not be quoted by an AI assistant either.
Decube treats the catalog and the lineage graph as the same object rather than two products stitched together. It connects to warehouses and lakehouses such as Snowflake, Databricks, BigQuery and Redshift, harvests metadata automatically, computes column level lineage from the query history rather than from hand drawn diagrams, and carries data quality monitoring and access governance on the same asset record.
The argument underneath the product is that a catalog without computed lineage is a directory. It can tell you a table exists and who owns it, but it cannot tell you what produced the number in the board pack. That is why automated column level lineage is the foundation Decube builds on, with data governance policy and ownership as the layer above it rather than the other way round.
One of the products that created this category. Alation built its reputation on behavioural analysis: it watches which tables people actually query and uses that to rank search results and suggest stewards, which is why it tends to get used rather than shelved.
Buyers frequently compare Alation with Zeenea, a European catalog built around a knowledge graph. The short version is that Zeenea is lighter, faster to deploy and priced more accessibly, while Alation carries more stewardship machinery and a deeper enterprise integration list. If the goal is self service discovery for a mid sized team, Zeenea often wins on time to value. If the goal is a formal stewardship programme across thousands of assets, Alation is the safer choice.
Computed lineage means the tool derives lineage from queries or code rather than asking a human to draw it. Quality monitoring means freshness and volume checks live in the same product. Access governance means it can grant, request or enforce access, not merely record the intended policy.
| Tool | Computed column lineage | Quality monitoring | Access governance | Open source |
|---|---|---|---|---|
| Decube | Yes | Yes | Yes | No |
| Alation | Partial | Partial | Partial | No |
| Atlan | Yes | Partial | Partial | No |
| Collibra | Partial | Yes | Yes | No |
| Informatica CDGC | Yes | Yes | Yes | No |
| Microsoft Purview | Partial | Partial | Yes | No |
| Databricks Unity Catalog | Yes | Partial | Yes | Partial |
| Snowflake Horizon Catalog | Yes | Partial | Yes | No |
| AWS Glue Data Catalog | No | No | Partial | No |
| dbt | Yes | Partial | No | Partial |
| OpenMetadata | Yes | Yes | Partial | Yes |
| DataHub | Yes | Partial | Partial | Yes |
| Amundsen | Partial | No | No | Yes |
| Secoda | Partial | Partial | Partial | No |
| data.world | Partial | Partial | Partial | No |
| OvalEdge | Partial | Partial | Yes | No |
| CastorDoc | Partial | No | No | No |
Eight of the seventeen publish something you can act on before a sales call. The rest quote, and the quote is driven less by the feature list than by two numbers: how many sources you connect and how many people need access.
| Tool | Published price? | How it is charged |
|---|---|---|
| Decube | Yes | Per user annually, from 21,000 USD a year, plus add-ons per source and monitor |
| Alation | No | Enterprise agreement, commonly six figures a year with implementation |
| Atlan | No | Enterprise agreement, scaled by sources and users |
| Collibra | No | Enterprise agreement, positioned at the top of the market |
| Informatica CDGC | No | Consumption units that vary by service |
| Microsoft Purview | Yes | Consumption for governance capabilities, some per user through Microsoft 365 |
| Databricks Unity Catalog | Yes | Included in Databricks consumption, no separate line |
| Snowflake Horizon Catalog | Yes | Included in Snowflake consumption, some features by edition |
| AWS Glue Data Catalog | Yes | Per object stored per month and per million requests, with a free allowance |
| dbt | Yes | Open source core free, hosted tiers per developer seat, enterprise quoted |
| OpenMetadata | Yes | Free under the Apache licence, managed service quoted |
| DataHub | Yes | Free and open source, managed cloud quoted |
| Amundsen | Yes | Free and open source |
| Secoda | Yes | Per user tiers |
| data.world | Yes | Published tiers for smaller teams, enterprise quoted |
| OvalEdge | No | Quoted by module and user count |
| CastorDoc | No | Quoted |
Two budgeting points are worth taking into the first call. First, implementation is routinely a large fraction of year one cost for the enterprise platforms, and it is often quoted separately, so ask for the total year one figure rather than the licence. Second, the free options are not free. Running OpenMetadata or DataHub properly means an owner, an upgrade cadence and someone on call, which is usually a fraction of an engineer indefinitely. That is frequently the right trade, but it should be a decision rather than a surprise.
Open source catalogs stopped being a compromise somewhere around 2024. The decision now is genuinely about where you would rather spend, and about who carries the risk when something breaks at month end.
| Consideration | Open source (OpenMetadata, DataHub, Amundsen) | Commercial (Decube, Alation, Atlan, Collibra and others) |
|---|---|---|
| Licence cost | None | Annual, usually five to six figures |
| Real cost | Engineering time, hosting and on call ownership | Licence, plus implementation services in year one |
| Time to first value | Weeks, if you have platform engineers | Days for the lighter tools, months for the enterprise suites |
| Support when it breaks | Community, or a paid managed offering | Contractual, with a named escalation path |
| Audit and evidence features | Present but usually shallower | The main reason regulated buyers pay |
| Customisation | Unlimited, because you hold the code | Bounded by the product roadmap |
| Risk that concentrates | Key person risk on whoever runs it | Vendor and renewal risk |
A rough decision rule that holds up in practice: if you have at least one platform engineer who can own the deployment for the next two years, open source is a defensible choice. If that person does not exist, or if a supervisor will ask you for evidence exports, the licence buys you something real.
Almost every article about data catalogs is written as though the only compliance driver is the European Union. For a great many teams the supervisor that matters is closer to home, and what they ask for shapes the shortlist more than the architecture does.
| Regulator | Who it covers | What it tends to ask for | The catalog capability that satisfies it |
|---|---|---|---|
| OJK, Indonesia | Banks, insurers and financial technology firms | Evidence of control and data quality over systems handling customer data, with local reporting and often local residency. | Lineage on reported figures, quality monitoring history, and deployment inside the customer environment. |
| APRA, Australia | Banks, insurers and superannuation funds | A named accountable owner for each system and demonstrable control over critical data elements. | Ownership on every asset record and a critical data element register with lineage. |
| MAS, Singapore | Financial institutions | Fairness, ethics, accountability and transparency for models affecting customers, and sound data management practice. | Traceability from a model input back to source, plus access and policy history. |
| NAIC, United States | Insurers, at state level | Documentation and governance of models used in underwriting and claims. | Column level lineage into model inputs and exportable point in time audit history. |
| EU AI Act | Systems placed on the European Union market | Risk classification, logging and record keeping. High risk obligations apply from 2 December 2027 for standalone systems and 2 August 2028 for systems embedded in regulated products. | A data provenance record for training and input data, retained and exportable. |
The practical consequence is worth stating bluntly. A catalog with excellent European regulatory templates and no answer for an Asia Pacific supervisor still leaves you doing the work in spreadsheets. Ask any vendor on your shortlist which of your regulators they have produced evidence for before, and ask for the export, not the dashboard.
Four things have changed in this category over the past two years and each of them should affect a shortlist drawn up now.
Most selection advice in this category is a feature matrix, which is not how the decision actually gets made. These six rules resolve it faster.
| If this is your situation | Start your shortlist here | Why |
|---|---|---|
| A supervisor will ask how a reported number was produced | Decube, Informatica CDGC, Collibra | All three can trace a figure to source columns and export the history. Discovery first tools cannot. |
| Analysts cannot find trusted data and nobody documents anything | Alation, Atlan, Secoda | Adoption is the whole problem, and these three win on search and interface. |
| Governance policy and accountability across thousands of assets | Collibra, Informatica CDGC | The workflow engines are the deepest available and the glossary machinery is mature. |
| Strong platform engineering team and no licence budget | OpenMetadata, DataHub | Both are credible enterprise catalogs. The cost moves to engineering time rather than disappearing. |
| Everything already lives in one cloud platform | Unity Catalog, Snowflake Horizon, Microsoft Purview | No integration work and no extra vendor, as long as nothing important sits outside it. |
| Small team, needs a working catalog this month | Secoda, OpenMetadata | Fast setup, and Secoda publishes pricing so budgeting does not need a sales cycle. |
| Definitions already live in dbt and the stack is modern | dbt plus one of Atlan, OpenMetadata or Decube | dbt produces excellent metadata but is not a full catalog. Pair it rather than replace it. |
Three mistakes account for most of the disappointment in this category. Buying a governance suite when the problem was discovery, which produces an expensive tool nobody opens. Treating a warehouse native catalog as an enterprise answer, which works until the auditor asks about the other platform. And deferring the source inventory until after procurement, which means the scope, the price and the timeline all get renegotiated at once.
There is no single best data catalog, and any list that names one is selling something. There are four reasonable answers depending on what you are solving. If the driver is analyst adoption, Alation and Atlan lead. If it is governance workflow at enterprise scale, Collibra does. If it is engineering ownership without licence cost, OpenMetadata and DataHub are genuinely credible. And if the driver is proving to a supervisor how a number was produced, the answer has to come from computed lineage rather than a policy register.
That last case is the one Decube is built for. Column level lineage computed from what actually ran, quality signals on the same asset record, and data governance policy layered on top rather than bolted beside it. If your next audit will ask where a reported figure came from, book a walkthrough with the Decube team and bring a real number to trace.
Data catalog tools are platforms that inventory every data asset an organisation holds and attach the metadata that makes each one usable: its source, owner, business meaning, freshness, lineage and access rules. They differ from data observability tools because the output is a searchable, governed record of what exists rather than a live check on whether the data is correct.
Start from the evidence you will be asked to produce rather than from a feature list. Write down the exact question a regulator, auditor or finance lead will ask, then shortlist the products that answer it. Count your data sources first, because both the quote and the implementation scale with that number. Then run one test in every demo: pick a real figure from a live dashboard and ask the vendor to trace it back to source columns on your data. That separates computed lineage from documented lineage faster than any matrix.
There is no single best tool because the category splits by problem. For analyst adoption and search, Alation and Atlan lead. For governance workflow at enterprise scale, Collibra does. For engineering led teams without licence budget, OpenMetadata and DataHub are credible. For regulated teams that must prove how a reported number was produced, a lineage first platform such as Decube fits better than a policy first one.
Alation is one of the strongest products in the category for discovery and stewardship. It ranks search results using which tables people actually query, which drives adoption, and it carries a mature stewardship model and a long enterprise integration list. Its trade offs are cost, an implementation that is a project rather than an install, and lineage depth outside its best supported sources that is often less complete than the demonstration suggests. It does not publish pricing.
Zeenea is lighter, faster to deploy and priced more accessibly, and its knowledge graph model suits self service discovery for a mid sized team. Alation carries more stewardship machinery, a deeper enterprise integration list and stronger usage based search ranking, which suits a formal governance programme across thousands of assets. If time to value matters most, Zeenea usually wins. If a stewardship programme with formal accountability is the goal, Alation is the safer choice. Neither publishes pricing.
Eight of the seventeen tools compared here publish a price. Decube publishes a per user rate, Microsoft Purview, AWS Glue, Databricks Unity Catalog and Snowflake Horizon are charged through platform consumption, while dbt, Secoda and data.world publish seat or tier pricing, and OpenMetadata, DataHub and Amundsen are free and open source. Everyone else quotes. Enterprise platforms such as Alation, Collibra and Informatica commonly require six figure annual commitments with implementation services on top. The number of connected sources and the number of users move the quote more than the feature list does.
A catalog supports dashboards in three ways. It records which table is the authoritative source for a metric, so two dashboards stop disagreeing. It carries lineage from the dashboard field back to the source column, so a number on a chart can be explained and a breaking change can be assessed before it ships. And it attaches ownership and freshness to the assets a dashboard reads, so a viewer can see that the underlying table failed its last load. Catalogs that integrate with the business intelligence layer surface this context inside the dashboard tool rather than requiring the viewer to visit the catalog.
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