TL;DR
Data catalog tools list every dataset a company owns and explain what each one means. Buyers choose between four kinds. Some are built for regulated industries that need audit trails. Some come free with the data platform you already pay for. Open source versions cost nothing to license but need engineers to run them. Check who owns a vendor first, because four independent catalogs were bought in 2024 and 2025.
Key Takeaways A data catalog answers five questions about every dataset. What exists, what it means, where it came from, who owns it, and who is allowed to use it. The market now splits into four categories, and the category you need is decided by your stack and your regulator, not by a feature list. Four independent catalogs were bought between September 2024 and December 2025. Zeenea, data.world, Select Star and Secoda all now sit inside larger platforms. Platform-native catalogs are usually already in your contract, so price a dedicated tool against what Purview, Unity Catalog or Horizon Catalog already give you. Almost no vendor publishes a price, so budget for implementation, metadata engineering and adoption alongside the license. Catalog programs usually fail on adoption. Naming a steward for every domain does more to fix that than any product feature. Watch on YouTube
Transform Your Data Strategy with Microsoft Purview
How a catalog, classification and policy layer changes the way governance actually runs day to day.
The Catalog You Bought Last Time Might Belong to Someone Else Now A data governance lead at a US insurer opened a renewal quote last spring and found a different company name on it. The catalog her team had rolled out two years earlier had been acquired, rebranded, and folded into a platform her organization did not use. Nothing broke that week, but the roadmap she had been promised no longer existed.
That story is not unusual any more. Between September 2024 and December 2025, four of the catalog vendors that appear on most shortlists were bought by larger platform companies. The buying decision has moved from picking the best product to picking a product that will still be independently supported in three years.
What Data Catalog Tools Actually Do A data catalog is an inventory of your organization’s data assets with enough context attached that someone who did not build a table can decide whether to trust it. The inventory part is easy to picture. The context part is where the products differ.
Strip away the marketing and every catalog is trying to answer the same five questions about every dataset you own.
What exists. Automated scanners crawl warehouses, lakes, BI tools and SaaS applications and register every table, column, dashboard and pipeline they find.What it means. A business glossary ties a column called cust_rev_ttm to an agreed definition of trailing twelve month customer revenue.Where it came from. Column-level data lineage traces a number on a dashboard back through every transformation to its source system.Who owns it. Stewardship assigns a named human to each domain, which is the part most programs skip and later regret.Who can use it. Classification tags sensitive fields, and data access governance turns those tags into enforced access rules rather than documented intentions.The first two are table stakes. The last three are where implementations succeed or quietly stall, and they are worth weighting heavily when you compare products.
Figure 1. The four layers every data catalog runs, with stewardship underpinning all of them. Data Catalog vs Data Dictionary vs Metadata Management vs Governance Platform These four terms get used interchangeably in vendor material, which makes shortlists harder to build than they need to be. They describe genuinely different scopes.
Capability Data dictionary Data catalog Metadata management Governance platform Primary job Define fields Find and trust data Operate on metadata Enforce policy Scope One system Whole estate Whole estate plus pipelines Whole estate plus people Population Manual Automated crawl plus curation Automated and event driven Inherits from the catalog Lineage None Table and column level Column level plus impact analysis Used as audit evidence Typical buyer A single data team Head of data or analytics Data platform engineering CDO, risk and compliance
Table 1. The four categories buyers most often conflate, and what separates them. In practice the boundaries blur. Most products sold as catalogs in 2026 also carry glossary, lineage and policy features, and most governance platforms ship a catalog inside them. Our deeper breakdowns of metadata management tools and data governance tools cover the adjacent categories if your requirement sits closer to either edge, and enterprise data catalog architecture covers how one is structured at scale.
Why the Catalog Market Consolidated Anyone rebuilding a shortlist from a 2024 article is working from a vendor list that no longer reflects who owns what. Four independent catalog vendors were acquired in fifteen months, and a fifth major player changed hands entirely.
HCLSoftware completed its acquisition of Zeenea in September 2024, and the product is now sold as Actian Data Intelligence . The old zeenea.com address redirects to Actian. ServiceNow closed its acquisition of data.world in July 2025 and now surfaces it as ServiceNow Data Catalog inside Workflow Data Fabric.
Snowflake announced its agreement to acquire Select Star in November 2025 , folding the product into Horizon Catalog. Atlassian acquired Secoda a month later. Separately, Salesforce completed its acquisition of Informatica on 18 November 2025 , which puts one of the largest catalog and governance vendors inside a CRM company.
Two things follow for a buyer. A product inside a platform vendor tends to get deeper integration with that platform and thinner integration with its competitors, so ask directly about the roadmap for connectors to stacks the new parent does not sell. And treat any comparison article that lists these vendors as independents as evidence it has not been updated.
Figure 2. Five acquisitions between September 2024 and December 2025 reshaped the shortlist. Data Catalog Tools Compared at a Glance Seventeen data catalog tools are compared below across four categories, with what each one is good at, who owns it today and how it is priced. The table doubles as a data catalog tools comparison you can hand to a procurement team, and ownership matters as much as capability now, so it gets its own column.
Tool Category Owned by Best fit Pricing model Open source Collibra Governance first Independent Regulated enterprises with formal stewardship Custom quote No Alation Governance first Independent Analyst-heavy orgs chasing adoption Custom quote No Informatica CDGC Governance first Salesforce Large multi-cloud estates with legacy systems Consumption credits No Ataccama ONE Governance first Independent Teams buying catalog and data quality together Custom quote No Atlan Active metadata Independent Snowflake, Databricks and dbt stacks Custom quote No ServiceNow Data Catalog Active metadata ServiceNow Organizations running on ServiceNow workflows Platform subscription No Secoda Active metadata Atlassian Small teams wanting fast AI documentation Per user No Select Star Active metadata Snowflake Fast automated lineage on a Snowflake estate Per user No Microsoft Purview Unified Catalog Platform native Microsoft Azure, Fabric and Microsoft 365 estates Consumption No Databricks Unity Catalog Platform native Databricks Lakehouse governance on Databricks Included with platform Yes, core project Snowflake Horizon Catalog Platform native Snowflake Snowflake-centric estates Included with platform No AWS Glue Data Catalog Platform native Amazon Technical metastore for AWS analytics Per object and request No OpenMetadata Open source Community, Collate Teams wanting catalog, lineage and quality in one Free, hosted option Yes DataHub Open source Community, DataHub Engineering-led teams building on APIs Free, hosted option Yes Apache Atlas Open source Apache Foundation Existing Hadoop and Ranger estates Free Yes OvalEdge Mid market Independent Mid-size firms needing governance workflows Custom quote No Dataedo Lightweight Independent Documentation and dictionary work Per user No
Table 2. Seventeen data catalog tools by category, ownership and pricing model, verified September 2026. Governance-First Data Catalog Tools These are the data catalog tools built for organizations where an auditor will eventually ask who approved a definition and when. They carry the deepest stewardship workflows, and they cost the most, which is why searches for the best data catalog software usually land here first.
Figure 3. Four categories, four different reasons to buy. Identify yours before you compare features. Collibra Collibra is the reference point for formal governance operating models. Its strength is workflow, meaning the ability to route a definition change through an approver, record the decision, and produce that record later as evidence.
It suits organizations that already have a governance council and need software to run it, which is why it scores well against a formal data governance maturity model . Teams without that structure often find it heavy, because the product assumes roles and processes that have to exist outside the tool. Collibra says it was named a Leader in the Gartner Magic Quadrant for Data and Analytics Governance Platforms published on 6 January 2026, its second consecutive year .
Alation Alation built its reputation on adoption. The search experience and the way it surfaces which datasets colleagues actually query make it the easiest of the governance-first tools to get analysts using voluntarily.
That focus is also its trade-off. Where Collibra starts from policy and works toward usage, Alation starts from usage and works toward policy, so organizations with strict enforcement requirements should test the policy side carefully. Alation reports being named a Leader for the fifth time in the Gartner Magic Quadrant for Metadata Management Solutions , which Gartner relaunched on 19 November 2025 after retiring it in 2020.
Informatica Cloud Data Governance and Catalog Informatica’s current catalog product is Cloud Data Governance and Catalog, which runs on the Intelligent Data Management Cloud. This matters because a great deal of published comparison content still names Enterprise Data Catalog, which is the older on-premises line rather than the product Informatica now sells.
The reason to shortlist it is connector breadth. If your estate includes mainframe, SAP, Oracle and three clouds, Informatica reaches more of it out of the box than anything else here. Informatica also reports Leader placement in the same January 2026 governance Magic Quadrant . The open question is what Salesforce ownership means for a roadmap that historically served every platform equally.
Ataccama ONE Ataccama is worth a look when data quality is the actual problem and cataloging is the means to fix it. The platform runs profiling, rule-based quality monitoring and cataloging on the same metadata, so quality scores appear next to the assets they describe.
Buyers who separate those two purchases often end up integrating two vendors later, so compare it against dedicated data observability tools and a data quality framework before deciding.
Ataccama remains independent, with Bain Capital Tech Opportunities holding a minority growth investment.
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Active Metadata and Modern-Stack Catalogs This group grew up around Snowflake, Databricks, dbt and cloud BI. The products assume an API-first stack, ship faster, and generally look better to analysts than the governance-first tier.
Atlan Atlan is the strongest independent option in this category and the one most likely to appear opposite Collibra in a final two. Its active metadata approach pushes context back out to where people work, so a definition can surface inside Slack or a BI tool rather than only inside the catalog.
Atlan says it moved from Visionary to Leader in the January 2026 governance Magic Quadrant , and it is also named a Leader in the November 2025 metadata management report . For a Snowflake or Databricks estate with a dbt layer, it is usually the shortest path to something analysts will use.
ServiceNow Data Catalog, Formerly data.world data.world brought a genuine knowledge-graph model to cataloging, which made relationships between assets query-able in ways relational metadata stores struggle with. ServiceNow closed its acquisition in July 2025 and now positions the product inside Workflow Data Fabric.
If your organization runs on ServiceNow, that integration is a real advantage. If it does not, weigh how much of the roadmap will now be spent on ServiceNow-specific work.
Secoda Secoda targets smaller data teams that want documentation generated rather than written. AI-drafted descriptions and a light deployment model let a five-person team stand something up in days.
Atlassian acquired Secoda in December 2025 and has signalled that it feeds the Rovo AI product. Teams already standardized on Atlassian may benefit; others should ask where standalone development sits on the roadmap.
Select Star Select Star’s differentiator was automated column-level lineage that worked with very little configuration. It was a common answer for analytics engineering teams that wanted lineage without a governance program attached.
Snowflake announced its acquisition in November 2025 and is folding the capability into Horizon Catalog. Plan for it as part of a Snowflake subscription.
Platform-Native Catalogs You May Already Own Before buying dedicated data catalog software, price it against what your existing data platform already includes. For a single-platform estate, the native option often covers enough of the requirement that a separate purchase is hard to justify.
Microsoft Purview Unified Catalog Microsoft’s catalog capability is called Unified Catalog, and it sits alongside the Purview Data Map that does the scanning. It organizes assets by governance domain and data product rather than by source system, which maps better to how business users think.
Purview automatically surfaces Microsoft Fabric item metadata into Unified Catalog and supports data quality assessment against Fabric Lakehouse tables. For an Azure and Microsoft 365 estate, that integration depth is difficult for an independent vendor to match, and it carries straight into Microsoft Fabric governance . Our guides to the Purview data catalog and Purview licensing go deeper on both.
Databricks Unity Catalog Unity Catalog is the governance layer for Databricks, handling table and column permissions, lineage and discovery across workspaces. Databricks open-sourced it in June 2024 under Apache 2.0, and the project is now hosted by the LF AI and Data Foundation.
That open-source status matters more than it first appears. The OSS project speaks the Iceberg REST Catalog and Hive Metastore APIs, so it can act as a neutral technical catalog for engines outside Databricks. It is not, however, a business glossary or a stewardship workflow tool. Our Unity Catalog versus Purview versus Collibra comparison works through where that gap matters.
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Databricks Unity Catalog Explained
What Unity Catalog governs, what it does not, and where a separate catalog still earns its place.
Snowflake Horizon Catalog Snowflake describes Horizon Catalog in its own documentation as the agentic catalog for all your data, whether it is inside or outside of Snowflake. It bundles discovery, classification, access policy and quality monitoring into the platform rather than selling them separately.
With Select Star folding in, its automated lineage should improve through 2026, and it pairs with the wider picture in Snowflake data governance . We cover the product in more depth in our Snowflake Horizon Catalog guide .
AWS Glue Data Catalog and Google Knowledge Catalog AWS Glue Data Catalog is a technical metastore rather than a business catalog. It registers schemas for Athena, EMR and Redshift Spectrum and does that job well, but it has no glossary, no stewardship and no policy workflow, so most AWS shops pair it with something else.
Google’s naming has changed twice and trips up a lot of published content. The legacy Data Catalog was deprecated in February 2025 and shut down on 1 June 2026, and its successor Dataplex Universal Catalog was renamed Knowledge Catalog in April 2026 . The APIs kept their old names, which is why the confusion persists.
Open-Source Data Catalog Tools and When They Win Open source removes the license line from the budget and replaces it with an engineering line. That trade works when you have platform engineers to spare and fails when you do not.
OpenMetadata OpenMetadata is the most complete open-source option, combining catalog, lineage, data quality and collaboration in one deployment with a large connector library. Teams that would otherwise buy two products can often cover both needs here.
DataHub DataHub came out of LinkedIn and is built around a metadata graph with a strong API surface. It is the right answer for engineering-led teams who intend to extend the model and push metadata in from their own pipelines rather than rely on packaged scanners.
Apache Atlas Atlas remains relevant mainly where Hadoop and Apache Ranger are still in production. Its tag-based policy integration with Ranger is genuinely useful in that setting and largely irrelevant outside it.
Amundsen Amundsen mattered a great deal in the late 2010s as a discovery-first catalog from Lyft. Fewer organizations start with it now because OpenMetadata and DataHub cover the same ground with broader governance features and more active development.
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Open Source Against Commercial, Honestly Factor Open source Commercial License cost None Annual subscription Time to first value Weeks to months Days to weeks Who runs it Your platform team The vendor Stewardship workflow Basic, often custom built Mature and configurable Audit evidence You build the reporting Packaged reports Customization Unlimited Within the vendor model Best for Engineering-rich teams, light regulation Regulated industries, thin platform teams
Table 3. The real trade-off is not price. It is where the effort lands. Mid-Market and Lightweight Options OvalEdge OvalEdge targets organizations that need governance workflows without enterprise pricing. It covers cataloging, access requests and stewardship at a scale that suits a few hundred users rather than a few thousand.
Dataedo Dataedo is best understood as documentation and data dictionary tooling rather than a governance platform. For a team whose real problem is that nobody has written down what the columns mean, it solves that directly and cheaply.
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What Data Catalog Software Costs in 2026 Mordor Intelligence sizes the data catalog market at USD 4.39 billion in 2026, growing to USD 10.75 billion by 2031 at a compound annual rate of 19.62 percent. Individual prices are much harder to find.
Why Almost Nobody Publishes a Price Enterprise catalog pricing depends on variables that differ wildly between buyers. Asset volume, user counts by role, connector requirements, which governance modules you turn on, and whether you need private deployment all move the number.
The practical consequence is that you cannot budget from a website. Get quotes from at least three vendors using the same asset counts and user counts so the numbers are comparable.
The Four Pricing Models You Will Be Quoted Per user, tiered by role. Creators and stewards cost more than read-only consumers. Model your consumer count carefully, because catalogs only pay off at wide adoption and a per-seat model can punish exactly that.Per asset. Priced on tables, columns or data sources registered. Predictable until someone points a scanner at a lake with a million objects.Platform subscription. A flat enterprise agreement, sometimes capacity based. Easiest to budget, hardest to negotiate down later.Consumption. Common for cloud-native catalogs, billed on scanning, storage and processing. Cheap to start and worth modelling at full estate scale before you commit.The Costs That Are Not on the Quote Cost area What to estimate Who usually pays for it Implementation Connector setup, environment config, SSO Partner or vendor services Metadata engineering Custom scanners for systems with no connector Your platform team Glossary build Agreeing and writing definitions Business domain owners Stewardship time Ongoing curation, roughly a day a week per domain Named stewards Change management Training, onboarding, internal comms Data office Administration Upgrades, scanner monitoring, access reviews Platform team
Table 4. Total cost of ownership items that rarely appear in a vendor proposal. Poor data quality is the cost most catalog business cases are written against. Gartner’s widely cited estimate, first published in its 2020 data quality research and still quoted on its own data quality topic page , puts the average at USD 12.9 million a year per organization. Gartner also reported in 2023 that 59 percent of organizations do not measure data quality at all.
For a more current read, dbt Labs surveyed 459 data practitioners for its 2025 State of Analytics Engineering report and found poor data quality named the most frequent challenge by 56 percent of them. That is the number a catalog business case should be built on, because it measures the problem as practitioners experience it today.
How to Narrow Seventeen Data Catalog Tools to Two Most evaluations stall because they start with a feature matrix. A feature matrix cannot separate seventeen products that all claim lineage, glossary and classification. Filters can.
Figure 5. Each filter is cheaper to apply than the one after it, so apply them in order. Start From the Problem, Not the Feature List Write down the single sentence that describes why this project is funded. Analysts cannot find data. Auditors keep asking for evidence we produce by hand. Sensitive fields are spreading and nobody knows where. AI projects keep stalling on untrusted inputs.
Each of those sentences points at a different category. Discovery problems point to active metadata tools. Evidence problems point to governance-first platforms. Sensitive-data problems point toward sensitive data discovery tools . AI readiness points at whichever catalog your model platform already talks to.
Filter on Integrations You Cannot Live Without List the systems that must be covered on day one, then strike every vendor that lacks a first-party connector for any of them. Custom connectors are possible and they are also a project nobody budgets for.
Check the same list against your data integration tools , since the catalog has to read whatever they produce. Pay particular attention to the awkward sources. SAP, mainframe, on-premises Oracle and older reporting tools separate the field faster than any cloud connector will.
Run a Proof of Concept on Your Own Metadata Vendor demos run on curated sample estates where lineage resolves perfectly. Load your own metadata instead, including the messy schema nobody has documented since 2019.
Measure four things. How long the initial scan takes, how accurate column-level lineage is on a pipeline you know well, how much of the glossary the tool can draft automatically, and how many clicks it takes an analyst to answer a real question.
Score the Final Two With Weights You Set First Agree the weights before you see the scores, otherwise the scoring rationalizes a decision already made. The weighting below reflects where catalog projects actually succeed or fail in delivery.
Stack compatibility, 25 percent Governance and stewardship workflow, 20 percent Adoption experience for non-technical users, 20 percent Metadata automation, 15 percent Total cost of ownership, 10 percent Vendor stability and support, 10 percent Vendor stability deserves its own line this cycle. Ask who owns the company, when the last funding or acquisition event was, and what the connector roadmap looks like for platforms the parent company competes with.
Which Catalog Fits Your Industry Regulatory context changes the answer more than company size does. These are the patterns we see most often in delivery.
Financial Services and Insurance Evidence production is the deciding requirement. Examiners want to see who owns a data element, how it is classified, when the classification last changed, and the lineage behind a reported figure.
That favors governance-first platforms with mature approval workflows, or Purview where the estate is Microsoft-centric and the bank wants classification and policy in the same place as its Microsoft 365 controls. A head-to-head sits in our Purview versus Collibra versus Alation comparison . Our data lakehouse guide for financial services covers the platform side of the same problem.
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Healthcare and Life Sciences Protected health information discovery comes first, and it has to work across clinical systems, imaging stores and administrative databases that were never designed to be scanned together. Classification accuracy matters more than search elegance here.
Look for strong automated classification, rule-based quality monitoring, and the ability to prove that a sharing arrangement was governed. Data governance in healthcare goes into the regulatory detail.
Manufacturing, Logistics and Retail The pressure here is operational rather than regulatory. Sensor and telemetry volumes make asset counts explode, so asset-based pricing models get expensive quickly and consumption models need careful modelling.
Prioritize scanners that handle semi-structured and streaming sources, and check how the tool handles a catalog with millions of low-value objects without burying the hundred that matter. Much of that volume is unstructured, which brings unstructured data governance into scope.
Telecom and Media Scale is the constraint. Subscriber and usage data produces the largest estates of any sector, and GDPR and CCPA compliance obligations sit on top of it.
Test search performance and scan duration at realistic volume during the proof of concept, because several products that feel fast on a ten-thousand-asset demo slow noticeably past a million.
Catalogs Are Becoming the Context Layer for AI Agents The strongest new argument for a catalog has little to do with human search. An AI agent asked a business question has to decide which table to query, what the columns mean, and whether it is allowed to read them. Those are exactly the three things a catalog stores.
Without that layer, an agent either guesses or gets pointed at a hand-curated subset that goes stale. With it, the agent inherits the same definitions, ownership and access rules the humans work under. Our deeper walkthrough of how that context layer fits inside an agent’s design lives in AI agent architecture .
This is why platform vendors bought catalogs rather than built them. Snowflake calls Horizon Catalog an agentic catalog in its own documentation, and Microsoft has wired Purview classification into how Fabric data is surfaced. Practically, three catalog features become gating requirements. Semantic or natural language search, a machine-readable metadata API, and policy-aware retrieval that filters results by the caller’s entitlements.
Figure 4. Know which rung you are on before you shortlist. Most tools are bought for a rung above the one the organization has reached. Our deeper treatment of the subject sits in the AI data catalog guide , which covers feature stores and model lineage alongside the discovery layer.
Five Ways Catalog Rollouts Fail The technology is rarely the reason a catalog program disappoints. These five patterns account for most of what we are called in to fix.
Nobody owns the metadata. The scanner runs, forty thousand assets appear, and none of them has an owner. A catalog with no named stewards decays into a search index over undocumented tables within two quarters, which is how a lake becomes a data swamp .The glossary is written by the wrong people. Definitions drafted by the data team and never ratified by the business get ignored the first time finance disagrees with one. Ratification has to happen before launch, not after.It is treated as a software install. Procurement, deployment and a training session is not a program. Without an operating model defining who approves what, the tool encodes no decisions.Everything gets cataloged at once. Scanning the whole estate on day one produces noise that buries the assets people actually need. Start with the domains behind your top reports, following established data governance best practices .Lineage is decorative. Lineage diagrams that look impressive but break at the first stored procedure give false confidence. Test lineage on your hardest pipeline during evaluation, not after signature.The common thread is that all five are organizational, and none is fixed by switching vendors. Our write-up on data governance challenges covers the same ground from the program side, and data stewardship covers the ownership question specifically.
How Kanerika Delivers Data Cataloging and Governance Buyers who decide they want data catalog services rather than a shelf product usually want three things delivered together, the catalog itself, the classification scheme behind it and the stewardship model that keeps it current. Kanerika is a Microsoft Solutions Partner for Data and AI with the Analytics Specialization, a Snowflake Select Tier Partner and a Databricks Consulting Partner. Most of our catalog work therefore happens on platform-native tooling rather than on a separate purchase, and the engagements that go well follow the same five stages.
The work is packaged as kanSuite, a modular set of governance services rather than a licensed platform. kanGovern covers governance strategy and enforcement, kanComply covers the regulatory compliance framework, and kanGuard covers unauthorized access prevention. All three are delivered on Microsoft Purview.
Assess. Inventory the estate and the regulatory obligations attached to it, then decide which of the four categories the requirement actually sits in before any vendor conversation.Design. Define governance domains, the steward roster by name, the classification scheme, and the approval path for a definition change.Build. Stand up scanning against priority sources, configure classification rules, and wire lineage through the transformation layers rather than accepting source-to-target only.Govern. Turn classifications into enforced policy, connect the catalog to access provisioning, and produce the reporting an auditor will ask for.Embed. Train stewards, publish the glossary where people already work, and measure adoption rather than asset count.White Paper
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A global bank with nearly 9,000 branches and 22,000 ATMs shows what that looks like in practice. Its data sat across SAP, Dynamics 365, CRM, Oracle and Netezza core banking systems and a centralized lakehouse, with sensitive-data classification being done manually and inconsistently across all of it.
Kanerika implemented discovery through the Purview Data Map to identify and classify assets automatically, used Purview policies to govern PII, PCI and PHI handling, and automated lineage across the lakehouse layers. The published outcomes were data classification accuracy improved by 72 percent, zero data breaches, and 100 percent adherence to compliance regulations .
A North American healthcare organization ran electronic health records, medical imaging and administrative databases across Azure Blob Storage, SQL and SaaS applications. A centralized Purview catalogue with a classification framework and defined steward roles produced a 35 percent increase in data accuracy and a 90 percent increase in compliance adherence .
What Actually Gets Configured Catalog buyers rarely see the configuration layer before they sign, and it is where most of the delivery effort goes. On a Microsoft Purview engagement the build breaks down into six concrete pieces of setup.
Collection hierarchy. Purview inherits permissions down a collection tree, so the tree has to mirror the ownership model before anything is scanned. Getting it wrong means re-registering sources later, because a source can only belong to one collection.Source registration and scan rule sets. Each source gets a scan rule set defining which file types and schemas are in scope and which classification rules run against them. Scoping scans to the schemas that matter is what keeps a scan from taking a weekend.Custom classification rules. The built-in classifiers cover common patterns such as credit card and national ID numbers. Domain identifiers, policy numbers, member IDs and internal account formats need regular-expression or dictionary rules written against real sample data, then tested for false positives before they drive policy.Scan triggers and incremental runs. Full scans are expensive, so schedule a full scan monthly and incremental scans weekly, and align the window with the source system’s own maintenance schedule rather than running everything overnight on the same day.Lineage capture across the gaps. Purview captures lineage automatically from Data Factory, Synapse and Fabric. Transformations that happen in stored procedures, notebooks or third-party tools produce a break in the graph, and those gaps have to be closed through the Apache Atlas-compatible REST API or left honestly visible rather than papered over.Access policy and stewardship roles. Classifications only matter once they drive something. Mapping sensitivity labels to access policies, and assigning domain stewards who can approve requests, is the step that converts a documented catalog into an enforcing one.The recurring technical failure is step three. Teams accept the default classifiers, see a low match rate on their own domain data, and conclude the tool is weak when the real gap is that nobody wrote rules for the identifiers their business actually uses.
The pattern in both is the same. Automated classification does the volume work, and named human ownership makes the result stick. Our data governance services page covers how the engagements are structured.
Wrapping Up The right data catalog is decided by three things. Which of the four categories your actual problem sits in, whether the platform you already pay for covers enough of it, and whether the vendor will still be independently supported when you renew.
Start by writing the one sentence that explains why the project is funded, then let that sentence eliminate three of the four categories. Filter the survivors on the connectors you cannot live without, and prove lineage on your own worst pipeline before you sign anything.
The tool matters less than the ownership model around it. A modest catalog with named stewards beats an excellent one nobody maintains.
Frequently Asked Questions
What is a data catalog tool? A data catalog tool is a searchable inventory of every dataset an organization owns. It records technical metadata such as schemas and column types, business context such as definitions and owners, and lineage showing where each figure came from. Teams use it to find trustworthy data without asking the engineer who built the table.
What is the best data catalog? No single catalog is best for everyone. Collibra and Alation lead where formal stewardship and audit evidence matter. Atlan fits modern Snowflake and Databricks stacks. Microsoft Purview Unified Catalog suits Azure and Fabric estates. Pick the category that matches your problem first, then compare two products inside it. Ownership matters too, since several once-independent vendors now sit inside platform companies.
What is a modern data catalog? A modern catalog populates itself. Scanners register assets automatically, machine learning drafts descriptions and classifications, and lineage is captured from pipelines rather than typed in. It also pushes context back out to Slack, BI tools and AI agents instead of waiting for people to visit the catalog. The measure of a modern catalog is how little manual curation it needs.
What is an AI-powered data catalog? An AI-powered catalog uses models to generate asset descriptions, suggest classifications, answer questions in natural language and recommend related datasets. The more important shift is the other direction. The catalog becomes the governed context layer an AI agent reads before deciding which table to query. Semantic search, a metadata API and policy-aware retrieval are the features to test.
Can a data catalog improve AI readiness? Yes, and it is becoming the main argument for buying one. An agent needs to know which table to use, what the columns mean and whether it may read them. A catalog stores exactly those three things, so it gives agents the same definitions and access rules people work under.
What are the 4 pillars of data governance? The four pillars most frameworks name are data quality, data security, data privacy and data management. Quality covers accuracy and completeness. Security covers access control and encryption. Privacy covers consent and regulated personal data. Management covers the lifecycle from creation through archival. A catalog is the system of record that makes all four visible in one place.
What are the 5 pillars of data governance? The five-pillar version adds people to the four. It lists data quality, data stewardship, data security, compliance and data architecture. Stewardship is the addition that matters most, because it assigns a named human to each domain. Governance programs usually fail on that pillar rather than on technology. Architecture covers how platforms and pipelines are designed to support the rest.
What are 5 examples of metadata? Column names and data types are technical metadata. Business definitions and owner names are business metadata. Row counts and last-refresh timestamps are operational metadata. Sensitivity labels such as PII are classification metadata. Query history showing which datasets people actually use is behavioral metadata. Good catalogs capture all five types rather than only the technical layer.
What should a data catalogue contain? It should hold schema details and storage locations, plain-language definitions, a named owner for every domain, column-level lineage, sensitivity classifications and freshness or quality indicators. Usage signals showing which assets people query most are what turn an inventory into something analysts trust. Anything it holds without an owner attached will drift out of date quickly.
What is a data catalog example? Microsoft Purview Unified Catalog is a widely deployed example. It scans Azure, on-premises and SaaS sources, captures metadata, applies classifications and renders lineage across the estate. Databricks Unity Catalog and Snowflake Horizon Catalog are platform-native examples. OpenMetadata is the most common open source one. Each scans sources, applies classification and renders lineage across the estate.
What are the tools used for cataloguing? Governance-first tools include Collibra, Alation, Informatica CDGC and Ataccama. Modern-stack tools include Atlan, ServiceNow Data Catalog and Secoda. Platform-native options are Microsoft Purview, Databricks Unity Catalog, Snowflake Horizon and AWS Glue. OpenMetadata, DataHub and Apache Atlas cover the open source side. Dataedo and OvalEdge serve smaller teams and mid-market governance programs.
Which data catalog tools support automated metadata discovery? Every tool on a serious shortlist scans sources automatically, so the question is how much it finds without help. Ask how many of your systems have a first-party connector and how often scans refresh. Then ask how much of the glossary the tool drafts on its own. Run the scan on your own messiest schema, never on the vendor’s demo estate.
What is the difference between a data catalog and a data dictionary? A data dictionary defines fields inside one system and is usually maintained by hand. A data catalog spans the whole estate, populates itself by scanning sources, and adds lineage, ownership and policy on top of definitions. A dictionary tells you what a column means. A catalog also tells you whether to trust it.
Which data catalog tools support request approval workflows? Governance-first platforms such as Collibra, Alation and Informatica CDGC ship mature access-request and approval routing. Microsoft Purview handles it through policy and governance domains. OvalEdge covers it at mid-market scale. Open source options usually need the workflow layer built or integrated separately. Confirm the approval record is exportable, since auditors ask for that evidence.
Who uses a data catalog? Analysts and data scientists use it to find datasets they can trust. Engineers use lineage to judge the blast radius of a schema change. Stewards curate definitions and ownership. Compliance teams pull evidence from it. Increasingly, AI agents query it before deciding which table to read. Adoption across those groups is the only honest measure of whether it worked.
How to build a data catalog? Start with the domains behind your most-used reports rather than the whole estate. Connect those sources, let scanners populate technical metadata, then have business owners ratify definitions before launch. Assign a named steward per domain, turn classifications into enforced policy, and measure adoption rather than asset count. Expanding domain by domain beats scanning everything on day one.
How to organize a data catalog? Group assets by business domain rather than by source system, because that matches how people search. Apply one consistent classification scheme across every domain. Certify a small set of assets as trusted so search results have a clear first answer. Retire stale assets on a schedule. Review the structure quarterly as domains and ownership change.
How to use a data catalog? Search using business language rather than table names. Read the description, owner and freshness indicator before you use anything. Check lineage to see what feeds the dataset and what depends on it. Request access through the catalog so the approval is recorded, then flag anything that looks wrong. Reporting a bad definition is how the catalog stays accurate over time.
How long does it take to implement a data catalog? A scoped first domain typically goes live in six to twelve weeks. Scanning and connecting sources is the fast part. Agreeing definitions, naming stewards and ratifying them with the business is what sets the timeline. Estate-wide coverage is a program measured in quarters, not a deployment. Budget stewardship time from the start rather than adding it later.
What are the 5 layers of a data platform? The common breakdown is ingestion, storage, processing, analytics and governance. Ingestion collects from batch and streaming sources. Storage holds raw and modeled data. Processing transforms it. Analytics serves reporting and models. Governance runs across all four, which is where the catalog sits. Governance is the layer most often added last and regretted first.
What is API cataloging? API cataloging inventories and documents the APIs an organization exposes, covering endpoints, authentication, request and response schemas, owners and version history. It solves the same discovery problem as a data catalog for a different asset type. Some data catalogs register APIs as first-class assets alongside tables. Treating both in one place gives a single view of what the organization exposes.
How much do data catalog tools cost? Almost no enterprise vendor publishes pricing. Quotes are built from asset volume, user counts by role, connector requirements and which governance modules you turn on. You will see four models, per user, per asset, flat platform subscription and consumption. Budget for implementation and stewardship time alongside the license. Get three quotes using identical asset and user counts so the numbers compare.
What are data catalog services and when do you need them? Data catalog services are a delivery engagement rather than a software purchase. A partner assesses the estate, designs the governance domains and classification scheme, configures the catalog and trains the stewards. You need them when the blocker is the operating model. Many organizations already own a platform-native catalog that nobody has staffed.
Which data catalog is best for Snowflake? Horizon Catalog is the usual answer for a Snowflake estate. It is included with the platform. It covers discovery, classification, access policy and quality monitoring. Snowflake also acquired Select Star in November 2025 and is folding that lineage capability in. Teams running Snowflake alongside other platforms often shortlist Atlan as well.
Which data catalog works best with Databricks? Databricks Unity Catalog governs tables, permissions and lineage across workspaces and is included with the platform. It was open sourced in June 2024 and now sits with the LF AI and Data Foundation. Pair it with Atlan or Collibra when you also need a business glossary and stewardship workflows. The open source project also speaks the Iceberg REST and Hive Metastore APIs.
Is Microsoft Purview a data catalog? Yes. Microsoft Purview Unified Catalog is the catalog capability, and Purview Data Map does the scanning that feeds it. It organizes assets by governance domain and data product, and it surfaces Microsoft Fabric item metadata automatically. For Azure and Microsoft 365 estates it is usually the starting point. Purview also supports data quality assessment against Fabric Lakehouse tables.
What are the best open source data catalog tools? OpenMetadata is the most complete, combining catalog, lineage and data quality with a wide connector library. DataHub suits engineering teams who want an API-first metadata graph they can extend. Apache Atlas remains useful in Hadoop and Ranger environments. All three trade license cost for engineering effort. Amundsen still runs in older deployments but attracts fewer new ones.
What is similar to Collibra? Alation is the closest direct comparison, with stronger adoption features and slightly lighter policy enforcement. Informatica Cloud Data Governance and Catalog competes on connector breadth. Atlan wins on modern cloud stacks. Microsoft Purview is the alternative when the estate is already Microsoft-centric and budget matters. Ataccama is the alternative when data quality is the real driver.
