TLDR: These three tools aren’t competing answers to the same question. Unity Catalog governs data inside Databricks for technical teams. Purview provides enterprise-wide visibility and compliance across Microsoft-aligned environments. Collibra handles policy workflows, stewardship, and business glossaries across heterogeneous landscapes. Many enterprises run all three, not because they couldn’t decide, but because each tool addresses a different governance layer. The real question isn’t which one to pick. It’s understanding which layer your organization is missing.
Key Takeaways Databricks Unity Catalog comes at no additional cost in Databricks Premium and Enterprise tiers. It governs data, AI models, and analytics within Databricks workspaces, and only within them. Microsoft Purview is the strongest fit for organizations invested in Azure, Microsoft Fabric, and Microsoft 365. It provides enterprise-wide data classification, lineage, and compliance across both Microsoft and non-Microsoft sources.Collibra offers the deepest governance workflow automation , including business glossaries, policy management, stewardship, and approval chains. Base pricing starts around $170,000/year with implementation timelines of 6 to 12 months for complex environments. Collibra Protect for Databricks went GA in Q4 2024, enabling direct column- and row-level access enforcement through Unity Catalog, connecting Collibra’s business-side policies to Databricks’ technical enforcement layer. The most common enterprise deployment isn’t a single tool. It’s Unity Catalog for operational lakehouse governance, Purview for cross-platform visibility and compliance, and Collibra where complex stewardship programs are required. According to WinWire’s 2024 analysis, 60% of enterprise leaders now rank data governance above AI enablement and data security as a strategic priority. Partner with Kanerika to Modernize Your Enterprise Operations with High-Impact Data & AI Solutions Call or Text Us Now
The Problem With “Pick One” When enterprises evaluate Databricks Unity Catalog vs Microsoft Purview vs Collibra, most comparison articles start in the wrong place. They treat all three as competing answers to the same question, “which data governance tool should we choose?”, then build a feature matrix and declare a winner.
That framing misrepresents what’s actually happening inside enterprise data teams .
Here’s a scenario that plays out more often than vendors would like to admit. A data engineering team builds out Unity Catalog. Access controls are clean. Lineage is tracking automatically across notebooks and pipelines. Their data engineers love it, and for eighteen months, governance feels solved. Then the compliance officer asks a simple question: “Can you show me the complete lineage of this field, from the source system, through Databricks, into the Power BI report that went to the board last quarter?
The answer is no. Unity Catalog shows everything inside Databricks with precision. But it has no visibility into what happened before data entered the lakehouse, or after it left for downstream BI tools .
So the team adds Microsoft Purview . Cross-platform lineage gets sorted. Sensitivity labeling across Azure storage and Power BI is in place. But a third problem surfaces: the business glossary built over two years in spreadsheets needs a proper home. So do data ownership policies, stewardship workflows, and the approval chains the governance committee has started requiring.
That’s the situation that eventually leads most mature data organizations to Collibra.
That story isn’t unusual. It plays out in most enterprises with a serious Databricks deployment . These three tools complement each other; they don’t compete. Understanding that upfront saves months of re-architecture work later.
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Precise design intent matters more here than feature lists. Each tool was built with a specific user and scope in mind, and understanding that shapes every downstream decision.
Databricks Unity Catalog is an operational metastore. It’s the native governance and security layer for the Databricks Lakehouse Platform. When data changes inside Databricks, whether a table gets modified, a notebook runs, or a new column appears, Unity Catalog captures it in real time . It manages fine-grained access controls, tracks lineage across Databricks workspaces, and classifies data at the column level. The target user is the data engineer or data scientist working inside Databricks daily. It’s free at Databricks Premium or Enterprise tier, which makes adoption a natural starting point for any Databricks-first organization.
Microsoft Purview was formed by merging Azure Purview (data cataloging ) with the Microsoft 365 Compliance Center. Its purpose is enterprise-wide governance across an organization’s entire data estate, including Azure storage, Fabric, Synapse, Power BI, SharePoint, Salesforce, and on-premises sources. The target user is a mix of compliance officers, business consumers, and IT administrators who need a single view across all data assets, not just those inside Databricks. For organizations running Microsoft Fabric, Purview’s integration is deep enough that governing a Fabric-based lakehouse without it creates real lineage and access control gaps.
Collibra was purpose-built for enterprise data governance from the start. It launched in 2008 with a focus on data stewardship, business glossaries, and policy management, the organizational and process side of governance, not just technical cataloging. Unlike a pure data catalog , Collibra’s core value is in workflow automation: who owns data, who approves access, how policies get enforced across an organization. Large regulated enterprises in financial services , healthcare, and pharma have historically been its core market, precisely the organizations where enterprise data governance carries genuine regulatory consequences. In Q4 2024, Collibra Protect for Databricks went GA, enabling access policies from Collibra to be applied directly to Databricks Unity Catalog, enforcing column and row-level masking. This finally connected where business teams define policy with where technical enforcement happens.
Feature-by-Feature Comparison Dimension Databricks Unity Catalog Microsoft Purview Collibra Primary scope Within Databricks only Across enterprise data estate Cross-platform with business context Target user Data engineers, ML engineers Compliance, IT, business consumers Data stewards, governance teams Data cataloging Strong (Databricks-native) Strong (Azure/M365-native) Comprehensive, requires configuration Data lineage Automatic, granular, real-time (within Databricks) Entity and column-level (cross-platform) Deep lineage, separate module pricing Access control Fine-grained row/column (Databricks) Role-based across Azure sources Business policy to UC via Collibra Protect Business glossary Minimal Moderate Core capability, industry leading Stewardship workflows Not available Limited Deepest, built for this AI governance ML model governance in Databricks Copilot/Azure AI governance AI model metadata catalog Pricing Included with Databricks Premium/Enterprise Consumption-based or M365-included ~$170K/year base; modules separate Implementation time Days to weeks 2 to 4 months (focused deployment) 6 to 12+ months Cross-platform support Databricks only Broad, Azure-first, non-Azure supported 120+ connectors, enterprise-wide Compliance depth Audit logs, GDPR/HIPAA via Databricks config GDPR, HIPAA, PCI-DSS built-in Deepest audit trail and policy enforcement
The table gives you the quick orientation, but the cells don’t explain what the differences mean operationally. Here’s what actually matters for each dimension.
Data Cataloging Unity Catalog builds its catalog automatically from what’s running in Databricks, including tables, schemas, notebooks, and ML models. No scanning schedule, no manual registration. The limitation is that the catalog reflects Databricks’ view of the world only. It doesn’t know about data sitting in Azure Blob Storage, a Salesforce object, or a legacy Oracle warehouse unless that data has been ingested into Databricks.
Purview takes the opposite approach: broad automated scanning across the estate. It connects to 200+ sources natively, including Azure Data Lake , SQL Server, Power BI, SAP, and Salesforce, and builds catalog entries through scheduled scans. The coverage is wide, but it’s a snapshot rather than a live view. Schema changes don’t surface until the next scan runs.
Collibra’s catalog is the most configurable of the three, which also makes it the heaviest to set up. Business users can enrich catalog entries with context Unity Catalog and Purview don’t capture, such as usage notes, data quality ratings, and related business processes. That enrichment is where Collibra’s value sits. But it requires human effort to maintain, and organizations that underinvest in data stewardship end up with a catalog that’s technically live but organizationally empty.
Data Lineage This is where Unity Catalog’s engineering is most impressive. Lineage is captured automatically at the column level as code runs, with no configuration, no tagging, no inference from query logs. When a data engineer wants to know exactly which upstream source contributed to a specific field in a downstream table, Unity Catalog answers that question in real time. The tradeoff: lineage stops at the Databricks boundary. Data flowing into Databricks from an ETL pipeline , or out of Databricks into Power BI, sits outside its view.
Purview tracks cross-platform lineage by connecting to data movement services like Azure Data Factory , Synapse Pipelines, and Power BI. End-to-end lineage across an Azure-native pipeline is well-supported. Lineage for non-Microsoft tools requires connectors and additional configuration, and column-level lineage outside Azure services is less consistent than Unity Catalog’s native capability.
Collibra’s lineage module is comprehensive and covers 120+ sources, but it’s priced separately from the base platform. For organizations where lineage is a daily operational tool rather than just a compliance artifact, that additional cost is usually justified. Collibra’s lineage is also the most presentable to non-technical audiences: business users and auditors can navigate it through the UI without needing SQL or Databricks access.
Access Control Unity Catalog’s access control is fine-grained and enforced at the compute layer. Row-level filters, column masking, and table-level permissions are defined once and applied across all Databricks workloads , including notebooks, SQL warehouses, and jobs, without needing separate permission configs for each. This is a genuine operational improvement over the older per-cluster permission model, where the same table could have different access rules depending on which cluster a user was running.
Purview doesn’t enforce access controls directly. It classifies and labels data , identifying what’s sensitive and where it lives, and passes that context to enforcement systems like Microsoft Entra ID, Azure Storage, and Databricks Unity Catalog. The distinction matters: Purview tells you what should be protected; Unity Catalog (or Entra) actually protects it.
Collibra Protect, which went GA in Q4 2024, changed the equation here. Before it existed, access policies defined in Collibra had no technical enforcement mechanism inside Databricks. They were documentation, not controls. Now, policies built in Collibra’s no-code interface sync directly to Unity Catalog and enforce at the data layer. For governance teams that want business owners to define access policies without involving data engineers for every change, this is a meaningful shift.
Business Glossary and Stewardship Business glossaries aren’t a technical feature; they’re an organizational one. Getting a large enterprise to agree on what “customer,” “revenue,” or “active user” means across business units is a governance challenge, not a data engineering challenge . Unity Catalog has minimal glossary capability by design; it’s not what it was built for. Purview has a basic business glossary that works for smaller programs but doesn’t support the approval workflows, term relationships, and stewardship assignment that larger governance programs require.
Collibra was built specifically for this problem. Its glossary supports hierarchical term structures, synonyms, related terms, and approval chains, so when a data steward wants to propose a new business term, the definition goes through a defined review process before it becomes official. That workflow matters in regulated industries where the definition of a term like “high-risk customer” has compliance implications.
Stewardship workflows follow the same pattern. Unity Catalog has none. Purview has basic data ownership assignment but no workflow automation. Collibra’s stewardship model supports domain assignments, steward notifications, escalation paths, and audit trails for every policy decision, the kind of documentation that satisfies both internal governance committees and external regulators.
Compliance Depth Unity Catalog generates audit logs for all data access within Databricks, which satisfies basic compliance needs for teams that only need to prove who accessed what inside the lakehouse. GDPR and HIPAA compliance requires additional configuration, as sensitivity classification and data masking need to be set up explicitly.
Purview’s compliance coverage is broader and more integrated. It connects to Microsoft’s compliance framework, supports sensitivity labeling that works across Teams, SharePoint, Power BI, and Azure simultaneously, and includes built-in regulatory templates for GDPR, HIPAA, and PCI-DSS. For organizations using Microsoft 365 as their collaboration layer, Purview’s ability to apply data protection policies across documents, emails, and structured data in one place is difficult to replicate with other tools.
Collibra’s audit trail is the deepest of the three. Every policy decision, stewardship assignment, governance workflow, and data access approval is logged with full context, who made the decision, when, and why. For regulated industries where regulators ask not just “was data accessed?” but “who authorized access to that data and through what process?”, Collibra’s audit depth is what differentiates it from the alternatives.
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The Three-Layer Architecture: How They Work Together Rather than a choice between these tools, the architecture most Databricks-heavy enterprises land on looks more like a stack with three distinct layers.
Layer 2: Enterprise Visibility and Compliance (Purview). Purview sits across the broader estate. It scans Unity Catalog metadata and brings it into an enterprise-wide catalog, alongside Azure storage, Power BI semantic models , Synapse, Salesforce, and on-premises sources. Compliance officers use this layer for audit trails, sensitivity labeling, and regulatory reporting. For Fabric deployments, Purview is the governance layer for OneLake by design. Kanerika’s Microsoft Purview consulting practice handles this configuration regularly for clients where audit-ready lineage is a compliance requirement.Layer 3: Business Governance and Stewardship (Collibra). Collibra sits at the top. Business glossaries, data ownership assignments, stewardship workflows, and policy approval chains live here. When a data steward needs to define what “customer” means across three business units, or when a governance committee needs to approve a data product before publication, this is where that happens. Through Collibra Protect, policies defined here flow down to enforcement in Unity Catalog.These layers connect rather than compete. Unity Catalog feeds metadata into Purview and Collibra. Collibra policies enforce through Unity Catalog. Purview provides the compliance reporting layer that sits over both.
What Actually Breaks When These Three Run Disconnected Most articles stop at “they’re complementary.” What they don’t document is what happens in practice when enterprises deploy these tools in isolation, which is how the majority of organizations start, and where most governance problems originate.
These aren’t hypothetical failure modes. They’re the patterns Kanerika’s teams encounter when clients come in to fix a governance architecture that’s technically in place but not functioning as one system.
1. Unity Catalog without Purview: the compliance blind spot: Unity Catalog governs everything inside Databricks with precision. But the moment a compliance officer or auditor asks a cross-system question, “show me the full lineage of this customer record from our CRM into the board report,” Unity Catalog goes silent. It can’t see what happened before data entered the lakehouse. It can’t trace what happened after data left for Power BI or downstream applications. Organizations running Unity Catalog alone often discover this gap for the first time during an audit, which is the worst possible moment. The operational impact is real: teams spend days manually reconstructing lineage that a connected Purview instance would surface in seconds. For Kanerika’s data governance in banking clients, this gap has shown up in regulatory examinations where field-level data provenance is a direct examiner requirement.
Purview is a catalog. It scans and represents the state of data assets across the estate. But it doesn’t enforce anything inside Databricks in real time. When Unity Catalog isn’t feeding it live metadata, Purview’s view of Databricks assets becomes stale the moment a schema changes, a new table appears, or a pipeline is modified. More critically, access policies defined in Purview have no direct enforcement mechanism inside Databricks without Unity Catalog as the enforcement layer. Organizations in this position often have a governance catalog that looks complete but doesn’t reflect what’s actually running in production, a data quality and compliance risk that tends to surface at the exact moment trust in the platform is most important.
3. Collibra without Unity Catalog integration: the policy-enforcement gap: Before Collibra Protect for Databricks went GA in Q4 2024, this was an extremely common problem. Organizations would define detailed access policies in Collibra, masking rules for PII, row-level filters for regional data restrictions, and then depend on data engineers to manually implement those same policies inside Databricks. Two systems. Two places where policy had to be configured. Two opportunities for drift. When Collibra’s business-defined policy said one thing and Unity Catalog’s technical enforcement did another, the governance program was theoretically sound and operationally broken. Collibra Protect now closes this gap. Policies defined in Collibra’s no-code builder sync directly to Unity Catalog enforcement. But organizations that implemented Collibra before this integration existed and haven’t connected it since are still running disconnected.
The most insidious failure mode isn’t missing a tool. It’s having all three and not connecting them deliberately. Metadata gets duplicated across all three platforms. Business glossary terms in Collibra don’t match the classification labels in Purview. Unity Catalog has column tags that nobody in Collibra knows exist. Data stewards maintain two separate systems of record. And when a governance audit arrives, three different tools give three partially overlapping answers to the same question. The fix isn’t more tooling. It’s a clear metadata ownership model: Unity Catalog as the source of truth for technical metadata, Purview as the enterprise catalog layer, Collibra as the business context and stewardship layer, with defined sync points between them. This is the architecture Kanerika’s data governance practice designs from the start, specifically to avoid the remediation work that comes from discovering it later.
Data Lineage: Where the Real Differences Appear Data lineage is worth examining closely. It’s the capability most teams underestimate, and the one that most often drives platform reconsideration when compliance deadlines arrive.
Unity Catalog automatically captures lineage across Databricks workspaces without any manual configuration. When a notebook reads from a table and writes to another, that relationship is recorded in real time. The lineage is granular, column-level, tracked as data moves through pipelines , notebooks, and jobs. This is a genuine technical advantage over external cataloging tools, which have to infer lineage through metadata scanning. For Kanerika’s clients building ML pipelines on Databricks, this automated lineage typically delivers its first ROI when a team traces a data quality issue in minutes rather than days.
Purview offers entity-level and column-level lineage across Azure services. End-to-end lineage from Azure Data Factory through Synapse into Power BI works reliably in Azure-native environments. Cross-platform lineage, tracing data from an on-premises SQL Server through Databricks into a Fabric report, is possible but requires careful configuration. For regulated industries that need to prove data provenance across hybrid environments, Purview’s lineage coverage for Microsoft workloads is difficult to match at its price point.
Collibra’s lineage capabilities are comprehensive and support 120+ data sources, but column-level lineage has historically been a paid add-on, a friction point for teams where lineage is central to daily compliance workflows. For organizations where lineage needs to be surfaced for business users, auditors, or governance committees, Collibra’s presentation layer and business context add value that Unity Catalog and Purview don’t provide. The tradeoff is cost and implementation time.
Deployment Complexity: What Implementation Actually Looks Like Stage Unity Catalog Microsoft Purview Collibra Initial setup Low, default for new Databricks accounts (post-Nov 2023) Moderate, Azure-native config, source registration High, taxonomy design, workflows, glossary build Time to first value Days to 2 weeks 2 to 4 weeks (focused use case) 3 to 6 months Ongoing admin overhead Low Low to moderate High, requires dedicated governance team Technical skills required Databricks platform familiarity Azure/M365 administration Data governance program management Business user onboarding Minimal, primarily technical users Moderate Significant, training and change management required Switching cost Low Moderate High, glossaries, workflows, org knowledge don’t transfer cleanly
The gap in “time to first value” is significant and rarely discussed in vendor materials. Organizations that implement Collibra without the governance maturity to support it, no dedicated stewards, no executive sponsorship, no existing taxonomy, end up with a platform that’s technically live but organizationally stalled. Kanerika’s initial assessments specifically evaluate whether an organization is ready for each tool, not just whether the features fit. That governance maturity check is often the most valuable part of the engagement.
Most organizations don’t start with a blank slate. They have Databricks running, data flowing, and a governance gap that’s become impossible to ignore. The question isn’t which tool to choose. It’s which tool to add next, and what signal tells you it’s time.
This is the rollout sequence Kanerika uses with Databricks-first clients. It’s not the only valid path, but it reflects what works in practice for organizations building governance progressively rather than all at once.
Phase 1: Start with Unity Catalog (Day 1 on Databricks). If your organization is running Databricks and Unity Catalog isn’t enabled, enable it now. It’s free at Premium or Enterprise tier, defaults on for accounts created after November 2023, and setup is measured in days rather than weeks. At this phase, the goal is technical governance: clean access controls, automated lineage within Databricks, column-level classification for sensitive fields, and a single place where data engineers manage permissions. You’re not solving enterprise governance yet. You’re building the technical foundation that every other governance layer depends on.
Add Purview when your data estate extends meaningfully beyond Databricks, or when you face your first cross-platform compliance requirement. Practically, this means when a compliance officer, auditor, or legal team asks for lineage or classification data that Unity Catalog can’t answer because it stops at the Databricks boundary. For most enterprises, this happens within 12 to 18 months of a serious Databricks deployment. For regulated industries in financial services or healthcare , it often happens sooner.
Phase 2: Layer Purview for Enterprise Visibility. At this stage, the integration work matters. Purview needs to scan Unity Catalog’s metastore and pull technical metadata into the enterprise catalog. Sensitivity labels, data classification, and lineage across Azure Data Factory, Synapse, Power BI, and Fabric sources get configured here. The goal is a single compliance-facing view across the full data estate, one that auditors can interrogate and compliance teams can maintain without depending on data engineers for every question.
A focused Purview deployment for a defined set of data sources, say all Azure-native workloads plus the Databricks Unity Catalog metastore, typically delivers value within four to six weeks. The mistake at this phase is trying to catalog everything at once. Start with the data assets that carry the highest regulatory exposure or the most active audit risk, prove the value there, then expand.
Add Collibra when your governance program outgrows what Purview’s catalog can support organizationally. The clearest signals: your business glossary has grown to a point where maintaining it in spreadsheets or Purview’s moderate glossary tooling is creating inconsistency; you have dedicated data stewards or a governance office that needs workflow tooling; or your compliance program requires policy approval chains, data ownership tracking, and stewardship documentation that neither Unity Catalog nor Purview were built to handle. For most organizations, this means governance maturity is already reasonably established, with an executive sponsor, a governance framework on paper, and people whose job includes managing data policy. If those things don’t exist, Collibra will stall.
Phase 3: Introduce Collibra for Organizational Governance. Collibra is the most implementation-intensive of the three. The first three to six months focus on taxonomy design, business glossary population, domain assignment, and stewardship workflow configuration before most business users see meaningful value. The payoff is a governance program that runs at the organizational level, not just the technical level: data ownership that’s enforced rather than aspirational, policy workflows that create audit trails, and a business glossary that reflects how the organization actually talks about data rather than how engineers tagged it.
With Collibra Protect connected to Unity Catalog, policies defined in Collibra’s no-code builder enforce directly in Databricks. That connection is the most important integration in the stack. It’s what closes the loop between where governance is designed (Collibra) and where it actually runs (Unity Catalog).
Phase Tool Added Trigger Typical Timeline 1 Unity Catalog Starting Databricks Day 1, weeks 2 Microsoft Purview First cross-platform compliance need 12 to 18 months post-Databricks 3 Collibra Governance program outgrows catalog tooling When stewardship team and exec sponsorship exist
What Real Users Actually Flag Analyst reports tell you where a product sits in the market. User reviews tell you what it’s like to live with it at 9pm on a Tuesday when something breaks.
Unity Catalog earns consistent praise for automated lineage and ease of access control within Databricks. The complaint that comes up most: it stops at the Databricks boundary. Teams that need a governance view across SAP, Salesforce, or legacy warehouses hit this wall quickly, and it’s not a configuration problem. It’s a scope limitation by design.
Microsoft Purview gets high marks from Gartner Peer Insights reviewers for setup speed and cost efficiency within Microsoft environments. The most cited frustration is catalog search. Users describe getting thousands of results back with no meaningful prioritization, which is a real problem for a platform whose job is data discovery. The broader pattern from reviews: Purview’s development investment seems to favor security and compliance over catalog usability. For compliance-driven organizations, that’s fine. For teams expecting a discovery-first experience, it’s a gap worth knowing about.
Collibra holds strong analyst recognition. It appeared as a Leader in the Forrester Wave: Data Governance Solutions , Q3 2025 and the Forrester Wave: Enterprise Data Catalogs, Q3 2024, making it the only vendor to hold a Leader position in both reports simultaneously. Operational complaints are consistent: search functionality is described as unintuitive, assets locked in approval workflows aren’t discoverable to data consumers, and business metadata enrichment still requires significant manual effort. ROI from Collibra, according to G2 review data, is typically realized at the 25-month mark, and depends heavily on organizational commitment, not just software deployment.
Industry-Specific Starting Points Industry Primary Tool Reason Common Addition Financial Services Collibra or Purview Regulatory audit trail depth, complex policy workflows Unity Catalog for Databricks workloads Healthcare / Life Sciences Collibra HIPAA compliance, clinical data governance complexity Purview for M365 and Azure compliance Manufacturing Purview Often Microsoft-aligned; operational governance less complex Unity Catalog where Databricks is the analytics platform Retail / E-commerce Purview or Unity Catalog Discovery and self-service drive the primary use case Collibra if regulatory scrutiny increases Technology / SaaS Unity Catalog Engineering-led culture, Databricks-first architecture Purview as compliance requirements mature Public Sector Purview FedRAMP alignment, Microsoft ecosystem investment Collibra for complex cross-agency governance programs
These aren’t fixed answers. A financial services firm running Databricks as its primary platform looks different from one split across on-premises and multiple clouds . But industry context is usually the most practical first filter.
Five Questions That Clarify the Decision Feature comparisons can send you in circles. These five questions tend to cut through faster. They’re what Kanerika’s team works through at the start of a governance architecture engagement.
1. How much of your data estate runs on Databricks? If Databricks is your primary platform and your governance needs are technical, access controls, lineage, classification, Unity Catalog is the starting point. It’s free, native, and precise for Databricks workloads.
2. Do you have Azure or Microsoft Fabric as a core infrastructure layer? If yes, Purview becomes highly relevant. The integration with Fabric, Synapse, and Power BI is deep enough that trying to govern those workloads without Purview creates real gaps, particularly for compliance reporting. Kanerika’s Microsoft Fabric implementations include Purview as the governance layer from day one.
3. Is your primary governance challenge technical or organizational? Technical (access controls, lineage, classification) means Unity Catalog and Purview cover this. Organizational (who owns which data, what does “customer” mean, who approves data products before publication) means Collibra addresses this directly. Unity Catalog and Purview have minimal capability in the organizational layer.
4. Do you have a dedicated data governance team? No dedicated team means Unity Catalog and Purview are manageable with existing platform resources. A full governance program with executive sponsorship means Collibra is viable and delivers meaningful ROI. Anything in between: start with Purview and assess Collibra readiness before committing.
5. What’s your realistic first-year governance budget? Under $100K: Unity Catalog (included with Databricks) and Purview (often in existing Microsoft licensing). $150K to $250K: Purview standalone or Collibra at the lower end. $300K+ over 18 months: a full Collibra implementation with professional services is in scope.
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The AI Governance Angle All three platforms are adding AI governance, and their approaches differ in ways that matter for 2026 planning.
Unity Catalog is the most technically integrated option for governing ML models within Databricks. Model metadata, experiment tracking via MLflow, and model registry access controls are all managed within Unity Catalog. Kanerika’s Databricks MLflow implementation practice covers how this fits into a broader ML governance architecture.
Purview has moved most aggressively on AI governance outside Databricks. As enterprises deploy Microsoft 365 Copilot across Teams, SharePoint, and business applications, Purview controls what data Copilot can access and generates audit trails for AI-assisted decisions. For organizations running Microsoft’s AI tools , this is an advantage neither Unity Catalog nor Collibra can fully replicate today. With over 1,000 AI-related regulations now active across 69 countries, the data governance trends shaping 2026 make this genuinely urgent.
Collibra positions itself as AI-ready through model metadata discoverability and contextualizing AI outputs within data lineage. For regulated industries building AI programs where explainability is a compliance requirement, Collibra’s approach fits existing governance workflows naturally.
What Kanerika Brings to This Decision Kanerika occupies an unusual position in this market. As both a certified Databricks consulting partner and a Microsoft Solutions Partner for Data & AI, Kanerika’s teams have implemented Unity Catalog, Purview, and Collibra integrations for enterprise clients without vendor alignment pushing toward any single tool.
For Unity Catalog implementations, Kanerika handles the full scope: workspace setup, access control configuration, lineage validation, and integration with Purview or Collibra depending on the broader governance architecture.
For Microsoft Purview, Kanerika’s practice includes three accelerators that reduce implementation time and extend governance coverage. KANGovern accelerates business glossary development with pre-built industry templates for healthcare, financial services, manufacturing, and retail, using AI to identify and suggest relevant business terms from existing documentation. KANGuard handles data security and access control, applying sensitivity labels, encryption, and access policies aligned to regulatory requirements. KANComply manages the compliance framework layer, mapping Purview policies to HIPAA, GDPR, and PCI-DSS requirements and generating audit-ready documentation.
For organizations running Collibra alongside Databricks, Kanerika handles the Collibra Protect integration, enabling business-defined access policies to enforce directly through Unity Catalog without requiring data engineering involvement for each policy update.
A documented Kanerika engagement with a leading bank used Microsoft Purview to transform data governance across a complex, multi-system environment.
The Cost of Getting This Wrong One number tends to reframe this entire decision. The IBM Cost of a Data Breach 2024 report puts the average enterprise breach at $4.88 million, up 10% from the year before. That covers detection, containment, notification, and disruption. It doesn’t include the regulatory fines, the reputational fallout, or the three years it takes to rebuild customer trust.
Governance gaps feed directly into that figure. When Unity Catalog access controls aren’t reflected in Purview’s compliance view, sensitive data gets accessed through paths the compliance team doesn’t know exist. When Collibra policies aren’t connected to Unity Catalog enforcement, PII masking rules that look correct on paper do nothing in practice. These are the exact failure modes documented earlier, and they’re also how breaches happen.
The math most data teams skip: Unity Catalog is free. Purview is often already in an existing Microsoft enterprise agreement. Collibra runs around $170K/year at base. The full stack costs a fraction of a single breach. That’s not a sales argument. It’s just the actual numbers, which are rarely put side by side.
The question “which of these three should we use?” is usually the wrong starting point. A more useful question is: which layer of governance is missing from our current architecture, technical enforcement, enterprise visibility, or organizational stewardship?
For most enterprises with Databricks at the core, Unity Catalog is already in place or should be the first move. It’s free, native, and genuinely excellent for what it was built to do. The question is what to add around it as the organization grows.
When the gap is enterprise-wide visibility or compliance reporting, especially across Microsoft Fabric and Azure workloads, Purview fills it. When the gap is organizational, who owns data, what terms mean, how policies get approved and enforced across business units, Collibra addresses what the other two tools were never designed to handle.
None of that requires choosing one and ruling out the others. The data governance pillars that underpin sustainable programs, people, process, and technology, rarely fit inside a single platform. Organizations moving toward data mesh architectures will find this three-layer stack maps naturally to domain-based ownership. And for teams still untangling where governance ends and data management begins, the distinction matters more than most vendor materials let on.
The governance architectures that hold up over time tend to be the ones built with clear layer logic from the start, not the ones that had the best individual tool.
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FAQs Is Databricks Unity Catalog free? Yes — it’s included at no additional cost for Databricks Premium and Enterprise tier accounts. For accounts created after November 8, 2023, it’s enabled by default.
Can Unity Catalog replace Microsoft Purview? For Databricks-only environments, Unity Catalog covers the technical governance layer well. But it has no visibility outside Databricks — it doesn’t catalog data in Azure storage, Power BI, Salesforce, or on-premises systems. Purview’s enterprise-wide scope and compliance features address a different and broader set of requirements.
How does Collibra integrate with Databricks Unity Catalog? Through two mechanisms: metadata ingestion via Collibra Edge (pulling tables, schemas, and columns from Unity Catalog into Collibra’s catalog) and policy enforcement via Collibra Protect, which went GA in Q4 2024. Protect allows column- and row-level access policies defined in Collibra to enforce directly in Databricks.
Do enterprises typically run all three tools together? Many do. The most common pattern is Unity Catalog for technical lakehouse governance, Purview for enterprise-wide catalog and compliance, and Collibra for business stewardship and policy management. These layers connect through native integrations rather than duplicating work.
How does Microsoft Fabric affect this decision? Significantly. Organizations building data lakehouses on Fabric are making a governance decision simultaneously. Fabric’s native Purview integration means that governing OneLake without Purview creates real lineage and access control gaps. Unity Catalog doesn’t integrate with Fabric natively. For Fabric-first organizations, Purview is the default governance layer — the question is whether Collibra is needed alongside it for organizational stewardship.
