Metadata Lakehouse vs Data Catalog: Architecture Guide 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 11th, 2026 | 12 min read

Quick answer: What's the difference between metadata lakehouses and data catalogs?

A metadata lakehouse stores metadata in open table formats like Apache Iceberg, making it queryable through standard SQL. A data catalog stores metadata in proprietary databases optimized for UI search and discovery.

  • Access method. Lakehouse: Direct SQL queries. Catalog: UI interfaces and custom APIs.
  • Storage format. Lakehouse: Open formats (Iceberg, Delta). Catalog: Proprietary databases.
  • Compute coupling. Lakehouse: Decoupled, any engine. Catalog: Coupled to platform.
  • Vendor lock-in. Lakehouse: Minimal, open standards. Catalog: Higher, closed systems.
  • Use cases. Lakehouse: AI context, cost optimization. Catalog: Discovery, governance workflows.

Below: key differences at a glance, architectural patterns, when to use which approach, the hybrid strategy, and how modern platforms deliver both.

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Metadata lakehouses vs traditional data catalogs: Key differences at a glance

Dimension Metadata Lakehouse Data Catalog
Storage Open table formats (Iceberg, Delta Lake) in object storage Proprietary databases optimized for search
Query Method Standard SQL on any compatible compute engine Platform-specific APIs and UI search
Primary Users Data engineers, platform architects, AI teams All data stakeholders from analysts to executives
Access Pattern Programmatic via SQL, no UI required UI-first with embedded workflows
Metadata Flow Bidirectional, supports write-back Primarily one-directional ingestion
Interoperability High, vendor-neutral formats Limited, platform-dependent
Setup Complexity Higher, requires infrastructure decisions Lower, managed service model
Cost Structure Storage + compute (pay per query) Subscription-based platform fee
AI Integration Native, metadata as training data API-based, requires custom integration
Governance Model Policy-as-code, SQL-based rules UI-driven workflows and approvals
Time Travel Native support via format features Limited, implementation-dependent
Scale Limits Billions of assets, compute scales independently Platform-specific, tied to service tier

What are the key architectural and functional differences?

Storage architecture and access patterns


Metadata lakehouses store all metadata types in open table formats on cloud object storage. Think S3, Azure Blob, or Google Cloud Storage with Apache Iceberg or Delta Lake providing ACID transactions, schema evolution, and time travel.

Any Iceberg-compatible compute engine queries this metadata directly using SQL. Teams analyze metadata like analyzing data itself, using familiar tools like Snowflake, Databricks, or Spark. The architecture decouples storage from compute completely.

Data catalogs function as centralized inventories stored in proprietary databases optimized for UI rendering and search operations. Users discover data through web interfaces, browse asset hierarchies, and collaborate through platform-specific features.

The catalog manages metadata lifecycle behind abstraction layers. Search functionality, visual lineage, and embedded documentation serve business analysts who need context without writing queries.

Query flexibility and metadata analytics


Lakehouses expose metadata through standard SQL. Teams write queries like analyzing any dataset—join lineage tables with quality metrics, aggregate ownership by domain, identify unused assets through usage patterns.

This enables metadata analytics scenarios: “Find tables consuming storage but receiving zero queries in 90 days” or “Calculate average data quality scores by domain.” According to Gartner’s 2025 research on metadata management, this query flexibility enables use cases impossible with traditional architectures.

Catalogs provide query capabilities through platform APIs. Each vendor implements different query languages and capabilities. Teams cannot use standard SQL tools or transfer query logic between platforms.

Metadata flow and AI integration


Lakehouses support bidirectional metadata flow. AI systems read context and write back enriched classifications or usage signals. This creates feedback loops where metadata quality improves continuously through machine learning.

External systems integrate naturally through standard SQL interfaces. Any tool speaking SQL participates in the metadata ecosystem without custom development.

Catalogs primarily ingest metadata one direction. Sources push updates through APIs, but writing metadata back requires platform-specific integrations. This makes AI enrichment and external tool participation more complex.

Discovery and governance capabilities


Lakehouses excel at complex analytical discovery. Data teams write SQL to find patterns or drive data-driven governance decisions. Platform teams measure metadata completeness by domain, track quality trends over time, identify optimization opportunities.

Policy implementation happens through code. Teams define rules in SQL or Python that evaluate metadata conditions and trigger actions. This scales to billions of assets through automated execution.

Catalogs prioritize intuitive search for all users. Business analysts type keywords and browse results through visual interfaces. The platform recommends related assets, shows popularity metrics, surfaces trusted data through algorithmic ranking.

Governance workflows happen through UI tools. Stewards review assets, approve glossary terms, manage policies through point-and-click interfaces. This approach works well for organizations preferring human oversight.

Cost optimization and AI readiness


Organizations use lakehouse metadata to drive cloud cost reduction. SQL queries identify expensive unused tables, reveal redundant dataset copies, highlight optimization candidates. Teams join metadata with cloud billing data to calculate per-asset costs.

The queryable format enables AI observability. Models access metadata directly to understand data lineage, data quality, usage patterns before making recommendations. Teams analyze which datasets contributed to model predictions, track data provenance for compliance.

Catalogs provide cost insights through built-in dashboards. The platform aggregates usage statistics and highlights potential savings, but teams cannot perform custom cost analyses without API integration.

When should you use metadata lakehouses versus data catalogs?

Choose metadata lakehouses when


  • Your organization needs deep metadata analytics. Platform teams want to measure governance maturity across domains, optimize cloud warehouse costs, or feed metadata to AI systems programmatically.

  • Development teams have strong SQL capabilities and prefer programmatic access. The lakehouse model requires comfort with query writing and data pipeline development.

  • You prioritize vendor independence and interoperability. Open formats ensure metadata remains portable across platforms and accessible to any tool supporting standard interfaces.

  • AI readiness drives your metadata strategy. Systems need bidirectional metadata flow where enriched context flows back from AI models and external tools.

Choose data catalogs when


  • Non-technical users dominate your data community. Business analysts, executives, operations teams need intuitive search without writing queries.

  • Visual discovery and collaboration matter more than analytical flexibility. Teams value user-friendly interfaces, embedded conversations, guided workflows over programmatic control.

  • Managed services align with your operational preferences. You want vendors handling infrastructure, scaling, maintenance rather than building metadata platforms internally.

  • Rapid time-to-value takes priority over architectural flexibility. Catalogs provide immediate discovery capabilities without infrastructure decisions or custom development.

Consider hybrid approaches for


  • Comprehensive metadata strategies that serve diverse user personas. Platform teams leverage lakehouse analytics while business users access catalog interfaces.

  • Organizations balancing governance rigor with user accessibility. Policy automation runs on lakehouse infrastructure while stewards manage exceptions through catalog workflows.

  • Environments with both technical and non-technical stakeholders. Different teams access the same metadata through interfaces matching their capabilities and workflows.

How does a hybrid metadata approach work in practice?

Architecture and synchronization


The hybrid model starts with metadata stored in open lakehouse format on cloud object storage. This foundation provides vendor-neutral ownership and analytical flexibility.

A catalog layer sits above the lakehouse, consuming metadata through standard interfaces. The catalog handles discovery, collaboration, workflow while the lakehouse enables analytics and AI integration.

Modern platforms sync metadata bidirectionally between layers. When analysts certify an asset through the catalog UI, that classification writes back to lakehouse tables. When platform teams update ownership through SQL operations, those changes reflect in the catalog interface immediately.

This keeps both layers consistent without manual reconciliation. The synchronization enables specialized tools to participate—data quality engines update freshness scores in the lakehouse, making that context available through catalog interfaces automatically.

Role-based access and governance


Different user groups access metadata through appropriate interfaces:

  • Data engineers query the lakehouse directly for pipeline development and troubleshooting using familiar SQL tools.
  • Business analysts use catalog search and browse features. They discover data, understand context, and request access without technical knowledge.
  • Data scientists leverage both layers. They discover datasets through the catalog, then analyze metadata via lakehouse SQL to validate model inputs.
  • Platform teams define policies as code in the lakehouse that execute automatically. Rules evaluate metadata conditions and trigger actions like asset reclassification or access reviews.
  • Stewards handle oversight through catalog workflows. They review policy exceptions, approve sensitive data access, manage glossary terms through guided interfaces.

This division scales governance effectively—automation handles routine compliance while human judgment resolves edge cases and strategic decisions.

How do modern platforms deliver both capabilities?

Modern platforms like Atlan’s architecture combine lakehouse power with catalog accessibility through layered design:

  • Open storage foundation. Metadata resides in Apache Iceberg tables on cloud object storage. This provides ACID transactions, schema evolution, time travel. Any Iceberg-compatible engine queries this metadata directly—organizations use existing Snowflake, Databricks, or Spark infrastructure.
  • Activation layer. Exposes metadata through multiple interfaces: REST APIs serve custom applications, SQL endpoints support analytical queries, MCP servers enable AI agents. This flexibility lets different systems access metadata through their preferred method.
  • Collaborative UI. User-friendly interface provides search, browse, collaboration features. Business users discover data through keyword search, visual lineage, curated collections. Stewards review data quality, approve glossary terms, manage access requests through guided interfaces.
  • Automated enrichment. AI-powered classification scans metadata, suggests tags, identifies sensitive data, recommends ownership based on usage patterns. Enrichment runs on lakehouse metadata directly, serving both analytical and catalog use cases. Organizations report 55% less time spent on metadata maintenance.
  • Policy orchestration. Governance policies execute as code against lakehouse metadata. Teams define rules using SQL or Python that evaluate conditions and trigger automated actions. The engine handles policy execution at scale, processing billions of assets.

This unified approach prevents organizations from choosing between analytical power and user accessibility—they get both through architectures that separate concerns while maintaining consistency.

What should organizations consider when choosing metadata approaches?

Team capabilities


Evaluate your team’s technical depth honestly. Organizations with strong SQL and data engineering capabilities can leverage lakehouse power effectively.

Teams dominated by business analysts and non-technical users benefit more from catalog-first approaches. Consider the learning curve and support requirements—lakehouses require infrastructure knowledge while catalogs abstract complexity but limit flexibility.

Vendor strategy


Determine your stance on vendor lock-in and platform portability. Organizations prioritizing independence should favor open lakehouse formats. Teams comfortable with vendor relationships and managed services may prefer integrated catalog platforms.

Evaluate how metadata strategy aligns with broader data platform decisions. If your data lakehouse uses Iceberg, metadata lakehouse makes sense.

Use case prioritization


List your critical metadata use cases. Analytics-heavy scenarios like cost optimization and AI training favor lakehouses. Discovery-focused needs like business user self-service and collaborative governance align better with catalogs.

Most organizations need both capabilities. The question becomes whether to implement separately or through integrated hybrid platforms.

AI readiness requirements


Consider how AI and automation factor into your metadata strategy. Organizations building AI agents and RAG systems need programmatic metadata access.

The bidirectional flow requirements of AI systems work better with lakehouse architecture. Catalogs can integrate but require additional development effort.

Real stories from real customers: metadata-driven governance at scale

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes

“Instead of spending 50 days manually identifying and then tagging personally identifiable information, Tide used Atlan Playbooks (rule-based bulk automations) to identify, tag, and then classify the data in a single, automated workflow.”

Michal Szymanski, Tide’s Data Governance Manager

Tide

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Key takeaways

Metadata lakehouses provide analytical flexibility and AI-native integration through open formats and SQL interfaces. Data catalogs deliver intuitive discovery and collaborative governance through managed UIs and workflows. Neither approach is universally superior—the right choice depends on team capabilities, use case priorities, and architectural preferences.

Organizations increasingly adopt hybrid approaches combining both capabilities. This strategy serves diverse user personas, balances automation with human oversight, prevents vendor lock-in while maintaining accessibility. Platform teams leverage lakehouse analytics while business users access catalog interfaces, all working from a unified metadata foundation.

The choice between approaches starts with honest assessment of technical capabilities, use case requirements, and AI readiness goals. Most enterprises benefit from architectures that provide lakehouse power for technical teams while offering catalog simplicity for business users.

Atlan delivers both metadata lakehouse analytics and intuitive catalog experiences in a unified platform.

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FAQs about metadata lakehouse vs data catalog

1. Can metadata lakehouses and data catalogs work together?


Yes, modern architectures often combine both. The lakehouse provides analytical foundation and AI integration while the catalog delivers user-friendly discovery and collaboration. Organizations sync metadata bidirectionally between layers, giving technical teams SQL access and business users intuitive interfaces.

2. Which approach better supports AI and machine learning?


Metadata lakehouses better support AI workloads. The open format enables direct SQL access for feature engineering, model training, and validation. AI systems can read and write metadata programmatically, creating feedback loops that improve context quality. Catalogs can integrate with AI through APIs but require additional development effort.

3. What are the cost differences between approaches?


Metadata lakehouses follow usage-based pricing with separate storage and compute costs. Organizations pay for object storage and query execution, optimizing costs by scaling compute independently. Data catalogs typically use subscription pricing based on user count or data volume. Total cost depends on usage patterns, team size, and infrastructure preferences.

4. How do governance capabilities compare?


Lakehouses enable policy-as-code governance that scales through automation. Teams define rules in SQL or Python that execute automatically across billions of assets. Catalogs provide workflow-driven governance through UI tools where stewards manage policies manually. Most organizations need both automated enforcement and human oversight.

5. Which approach is easier to implement?


Data catalogs are easier to implement initially. Managed services handle infrastructure, scaling, and maintenance. Organizations connect sources and start discovering data quickly. Metadata lakehouses require infrastructure decisions around storage, compute engines, and access patterns.

6. Can you migrate from one approach to another?


Migration difficulty depends on metadata format and vendor lock-in. Moving from closed catalog systems to open lakehouses requires exporting metadata and rebuilding integrations. Moving from lakehouses to catalogs is easier since source metadata remains accessible.

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