Data Catalog Migration Best Practices: A Complete Guide for 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 1st, 2026 | 30 min read

Without a proper migration strategy, many data catalog initiatives miss their objectives, often resulting in significant rework costs that can run into the millions of dollars. Data catalog migration has two meanings in practice: migrating your catalog platform itself (moving from Alation or Collibra to a modern solution) or using a catalog to de-risk broader data migrations like moving to Snowflake or Databricks. Both require metadata-first planning and phased execution. Done right, catalog migrations typically take 3–6 months and materially reduce downstream data issues. You’ll learn how to assess your current environment, execute migrations in phases, and validate success through measurable criteria.

Two Types of Data Catalog Migration

Migration Type What It Means Typical Timeline Primary Goal
Platform Migration Moving from one catalog tool to another (e.g., Alation→Atlan, consolidating multiple tools) 3-6 months Unified governance, better automation, modern capabilities
Enablement for Broader Migrations Using a catalog to support data/cloud migrations (e.g., Snowflake migration, warehouse modernization) Ongoing (6-18 months for full data migration) Maintain governance, accelerate discovery, reduce migration risk

This distinction matters because your approach changes dramatically. Platform migrations focus on preserving metadata investment while gaining new capabilities. Migration enablement treats the catalog as your control plane during broader transformations. Many organizations need both, replacing a legacy catalog while simultaneously using it to guide cloud migration.

Why do data catalog migrations fail?

Catalog migrations frequently fail due to three preventable mistakes: underestimating metadata complexity, treating migration as an IT‑only project, and attempting big‑bang cutovers. These failures can cost organizations millions of dollars in rework and often extend timelines by 6–12 months. Understanding what goes wrong helps you avoid the same traps.

Failure Pattern 1: Treating migration as “lift and shift”


Most teams assume metadata will transfer directly between systems. It doesn’t. Legacy catalogs store business context (glossary terms, descriptions, ownership) in proprietary schemas that don’t map cleanly to modern data catalogs. When you try to move everything wholesale, custom fields disappear, relationships break, and enrichment gets lost.

Organizations that skip metadata assessment often find themselves spending significantly more time troubleshooting post‑migration. The fix is metadata-first planning: inventory what you have, map transformations explicitly, and prioritize business context over technical schemas. Technical metadata (table structures, column types) can be re-ingested from source systems through connectors. Business metadata takes years to rebuild if lost.

Failure Pattern 2: No stakeholder alignment


IT teams often drive catalog migrations as infrastructure projects. Business users disengage when they’re not involved early, and adoption collapses post-migration. Without clear governance roles and decision rights, the new catalog becomes another unused tool.

Successful migrations establish cross-functional steering committees before assessment begins. Include data stewards, business domain leaders, analytics teams, and compliance representatives. These groups define success criteria, prioritize use cases, and champion adoption. Organizations with active steering committees see 3x higher post-migration adoption rates compared to IT-led initiatives.

Failure Pattern 3: Big-bang cutover approach


Switching from old catalog to new catalog in one weekend creates a single point of failure. When issues arise (and they will), your entire organization loses access to metadata. Teams scramble to troubleshoot while business-critical dashboards break.

Phased rollouts with parallel runs substantially reduce the number and impact of incidents. Start with read-only discovery and search, then migrate collaborative features like comments and queries, and finally transition governance workflows like access requests and policy enforcement. Running both catalogs for 1-3 months costs more in the short term but prevents catastrophic failures.

Failure Pattern Why It Fails Success Pattern Key Metric
“Lift and shift” approach Metadata schemas don’t directly map between platforms Metadata-first planning with explicit transformation mapping 40% faster migration
IT-only project without business involvement Low user adoption, governance gaps persist Cross-functional steering committee from day one 3x higher adoption rates
Big-bang cutover (switch everything at once) Single point of failure, no rollback plan Phased rollout with parallel runs for 1-3 months 60% fewer incidents

What are the phases of a successful data catalog migration?

Successful migrations follow a four‑phase approach spanning 3–6 months: Assessment (2–4 weeks), Planning (4–6 weeks), Execution (6–12 weeks), and Validation (2–4 weeks). Phased migrations typically complete faster than big‑bang approaches and significantly reduce failure rates. Each phase has specific deliverables and success criteria.

Phase 1: Assessment & Discovery (2-4 weeks)


Start by cataloging your current state. How much metadata exists? What’s enriched versus raw technical schemas? Who uses the catalog and for what workflows? This phase produces a complete inventory of assets, custom fields, integrations, and stakeholder requirements.

Key activities include metadata profiling (count of assets, enrichment levels, custom taxonomy), dependency mapping (identify downstream consumers like BI tools and data pipelines), and stakeholder interviews (understand pain points and success criteria). Organizations that document these findings reduce planning time by 30% and avoid surprises during execution.

Phase 2: Planning & Design (4-6 weeks)


Planning converts your assessment into an executable roadmap. Define how metadata will transform between systems, which assets migrate first, and what success looks like. This phase delivers transformation mappings, phased rollout plans, and validation criteria.

Build explicit transformation logic for business metadata (how glossary terms map to new taxonomy), governance metadata (how policies and classifications transfer), and social metadata (how to preserve user queries and comments). Prioritize assets using the 80/20 rule: focus on the 20% of data that drives 80% of business value. Document success metrics like metadata fidelity targets (95%+ preservation), user adoption goals (20-30% monthly active users within first quarter), and performance benchmarks (2x faster search than legacy system).

Phase 3: Execution (6-12 weeks)


Execute your migration in waves, not all at once. Most organizations migrate by domain (finance first, then marketing, then operations) or by use case (discovery and search first, then collaboration, finally governance workflows). Each wave includes metadata export from the legacy system, transformation and loading into the new platform, and validation checkpoints before proceeding.

Run both catalogs in parallel during this phase. Users access read-only content in the new catalog while still using the old catalog for active workflows. Monitor adoption metrics weekly and address friction immediately. Common issues include missing enrichment, broken lineage paths, or confusion about which catalog to use. Address these before expanding to the next wave.

Phase 4: Validation & Optimization (2-4 weeks)


Validation confirms the migration succeeded before decommissioning the old catalog. Test metadata fidelity (compare enrichment levels pre and post-migration), lineage accuracy (trace critical data flows end-to-end), user acceptance (measure engagement and satisfaction), and performance (benchmark search speed and query response times).

From there, optimize based on findings. Common optimizations include enriching additional assets through AI-powered recommendations, fixing broken lineage connections, and training users on new features. Successful migrations achieve 95%+ metadata fidelity and 80%+ user satisfaction within the first quarter.

Phase Timeline Key Activities Success Criteria Owner
Assessment & Discovery 2-4 weeks Metadata inventory, dependency mapping, stakeholder interviews Complete asset inventory, documented pain points, stakeholder buy-in Migration Lead + Data Governance team
Planning & Design 4-6 weeks Transformation mapping, phased rollout plan, success metrics definition Executable roadmap, transformation logic documented, validation criteria agreed Migration Lead + Architecture team
Execution 6-12 weeks Phased metadata migration, parallel runs, validation checkpoints Each wave validated before next, user adoption tracking Migration Lead + Engineering + Data Stewards
Validation & Optimization 2-4 weeks Fidelity testing, lineage validation, user acceptance, performance benchmarks 95%+ metadata fidelity, 80%+ satisfaction, performance targets met Migration Lead + Quality Assurance

How do you assess your current data catalog environment?

Assessment takes 2-4 weeks and involves three activities: metadata inventory (catalog all assets, schemas, custom fields), dependency mapping (identify downstream consumers and critical workflows), and stakeholder interviews (understand pain points and success criteria). Organizations that skip assessment face much higher failure rates because they encounter surprises during execution.

Metadata inventory: What you have


Start by quantifying your metadata. The average large enterprise has 50,000-500,000 cataloged assets spread across data warehouses, lakes, BI tools, and pipelines. Document how much is enriched (has descriptions, ownership, glossary terms) versus raw technical schemas. Identify custom fields and taxonomies your organization has built.

Export sample metadata to understand structure. Most legacy catalogs use proprietary schemas that won’t map directly to modern platforms. Seeing the actual data structure (how glossary terms are organized, how lineage is stored, how custom metadata is attached to assets) informs your transformation planning.

Dependency mapping: Who consumes your catalog


Your catalog doesn’t exist in isolation. BI tools query metadata to show dataset descriptions. Data pipelines use lineage to understand dependencies. Data scientists search the catalog to discover relevant features. Map these integrations before migration—breaking them creates organization-wide disruption.

Common dependencies include BI tools (Tableau, Power BI, Looker reading metadata via APIs), orchestration systems (Airflow using lineage for impact analysis), data quality tools (consuming classifications and policies), and custom applications (built on catalog APIs). Document which integrations are mission-critical and which can be rebuilt post-migration.

Stakeholder analysis: Understanding requirements


Interview representatives from each user group: data engineers (need technical metadata and lineage), analysts (need business context and search), data stewards (need governance workflows), and executives (need metrics on data usage and quality). Their requirements often conflict—engineers want automation while stewards want control—so prioritize explicitly.

Capture pain points with the current catalog. Common complaints include poor search relevance, slow metadata loading, missing lineage for key pipelines, and low adoption among business users. These become your success criteria for the new platform.

What metadata needs to be migrated?

Five types of metadata require migration: technical metadata (schemas, tables, columns), business metadata (descriptions, glossary terms, ownership), operational metadata (lineage, usage statistics, quality scores), social metadata (comments, tags, queries), and governance metadata (policies, classifications, access controls). Organizations often underestimate business and social metadata—these drive adoption. Social metadata can dramatically increase post‑migration adoption because users see familiar content when they arrive.

Technical metadata: The foundation


Technical metadata includes database schemas, table structures, column data types, and constraints. This is easiest to migrate because modern catalogs re-ingest it directly from source systems through connectors. You don’t need to export table definitions from your old catalog—just point the new catalog at Snowflake or Databricks and let automated discovery rebuild the technical layer.

Focus migration effort on custom technical metadata: tags your team added, notes about deprecated fields, or manual corrections to auto-discovered schemas. This enrichment doesn’t exist in source systems and must be explicitly transferred.

Business metadata: What drives discoverability


Business metadata includes glossary terms, plain-language descriptions, ownership assignments, and domain classifications. This is what makes your catalog useful to non-technical users. Without it, your new catalog becomes a technical inventory nobody uses.

Prioritize glossary migration first. Export your complete glossary hierarchy (terms, definitions, relationships between concepts) and transform it to match the new catalog’s taxonomy model. Then migrate asset-level enrichment: descriptions, README documents, and ownership mappings. This layer takes the longest to rebuild if lost, so verify preservation early in validation.

Operational metadata: Building trust


Operational metadata includes data lineage (how data flows from source to consumption), usage statistics (who accesses what and when), and data quality scores (freshness, completeness, accuracy). This metadata builds trust—users believe data is reliable when they see its complete lineage and quality metrics.

Lineage is particularly critical. Most legacy catalogs track table-level lineage but miss column-level transformations. Modern catalogs trace lineage at the column level, showing exactly how a given field in a BI dashboard derives from source tables. You can’t migrate this directly—new catalogs must re-parse SQL and ETL logic to build accurate lineage. Plan for 2-4 weeks of lineage validation after initial migration.

Social metadata: The adoption driver


Social metadata includes user-generated comments, saved queries, frequently accessed assets, and community discussions. This metadata increases adoption by 2.5x because new users see an active, engaged community rather than an empty tool.

Many organizations skip social metadata migration because it’s hard to export and seems less critical. This is a mistake. When users move to a new catalog and see their colleagues’ comments and queries preserved, they trust the platform immediately. When everything looks sterile and unused, adoption suffers.

Governance metadata: The compliance requirement


Governance metadata includes data classification labels (PII, sensitive, public), access policies (who can see what), compliance tags (GDPR-relevant, SOX-controlled), and approval workflows. This metadata isn’t optional—regulatory requirements mandate certain classifications and access controls.

Export governance metadata early and validate thoroughly. A misclassified sensitive field that becomes publicly visible creates compliance violations. Test access controls in the new catalog before cutover to ensure policies enforce correctly.

Metadata Type Business Value Migration Effort Priority Common Pitfall
Technical Low (easy to rebuild) Low (connectors automate) Medium Skipping custom enrichment
Business High (drives discovery) Medium (requires transformation) Highest Losing glossary structure
Operational High (builds trust) High (lineage requires re-parsing) High Accepting table-only lineage
Social Very High (drives adoption) Medium (export often limited) High Treating as “nice to have”
Governance Critical (compliance mandate) Medium (transformation + validation) Highest Inadequate testing

Platform Migration: How do you migrate from one catalog to another?

Platform migrations (moving from Alation or Collibra to modern catalogs like Atlan) require a three-step process: glossary and enrichment export (2-3 weeks), asset and lineage re-ingestion via connectors (3-6 weeks), and validation plus cutover (2-4 weeks). Use a dedicated migration team and plan for 3-6 months total. Organizations migrating to modern catalogs report materially faster time‑to‑insight post‑migration because automated features reduce manual metadata work.

Step 1: Export business context (2-3 weeks)


Your first priority is preserving the human-generated context that doesn’t exist anywhere else. Export your glossary structure (terms, definitions, hierarchies), asset enrichment (descriptions, README documents, ownership), and governance metadata (classifications, policies). Most legacy catalogs provide CSV exports or APIs for this data.

Transform exported metadata to match the new catalog’s schema. For example, if your old catalog uses a three-level glossary hierarchy (Business Area > Domain > Term) and the new catalog uses two levels (Domain > Term), you’ll need mapping logic. Document these transformations explicitly—manual fixes after migration create inconsistencies.

Step 2: Re-ingest technical metadata (3-6 weeks)


Don’t try to migrate table schemas and column definitions from the old catalog. Instead, point the new catalog’s connectors at your source systems (Snowflake, Databricks, BigQuery, BI tools) and let automated discovery rebuild the technical layer. Modern catalogs use out-of-the-box connectors that require no custom code.

This approach gives you cleaner, more accurate technical metadata than your legacy catalog had. Source systems change constantly, so metadata becomes stale. By re-ingesting from source, you get current schemas, real-time lineage, and up-to-date usage statistics.

Step 3: Validate and cutover (2-4 weeks)


Run both catalogs in parallel while users validate the migration. Start with a pilot group (20-30 users representing different roles) who test discovery, search, lineage, and governance workflows in the new catalog. They’ll identify missing enrichment, broken integrations, and workflow gaps before you migrate the full organization.

Measure validation success through specific metrics: metadata fidelity (95%+ of descriptions and glossary terms preserved), lineage completeness (all critical data flows traced end-to-end), user acceptance (pilot group rates new catalog as equal or better), and performance (search and query response times meet or exceed legacy system).

Once validation passes, execute cutover by domain or team. Migrate one business unit at a time, monitor for issues, and address problems before expanding. This phased approach takes longer but reduces risk significantly.

Approach Timeline Risk Level Best For Key Consideration
Big-bang cutover 1-2 months High Small catalogs (<10K assets) Single point of failure, no rollback plan
Phased by domain 3-6 months Medium Medium catalogs (10K-100K assets) Requires domain coordination, clear handoffs
Parallel run 4-8 months Low Large catalogs (100K+ assets) Higher short-term cost, much safer

Migration Enablement: How do data catalogs support broader data migrations?

When migrating data platforms (moving to Snowflake, Databricks, or cloud warehouses), catalogs provide three critical functions: pre-migration discovery (identify what to migrate and understand dependencies), during-migration governance (maintain access controls and track lineage across old and new systems), and post-migration validation (ensure quality and rebuild trust). Organizations using catalogs often see meaningfully shorter migration timelines and fewer post‑migration issues because unified metadata accelerates every phase.

Pre-migration: Discovering what to move


You can’t migrate effectively without knowing what you have. Data catalogs provide automated discovery across your entire data estate, showing tables, dashboards, models, and pipelines with usage context. This answers critical questions: Which datasets are actively used? Which are stale and can be decommissioned? What depends on what?

Use the catalog to prioritize migration waves. Start with high-usage, mission-critical datasets that deliver immediate business value. Leave low-usage or deprecated assets until later (or never migrate them at all). This approach shortens timelines and reduces cost—you migrate only what matters.

The catalog also reveals hidden dependencies. A dataset might have low direct usage but feed 15 downstream reports. Without lineage, you’d migrate that dataset late and break everything downstream. With lineage, you migrate dependencies in the right order.

During migration: Maintaining governance across hybrid environments


Cloud migrations take 6-18 months. During this time, you’re running hybrid environments—some data in the old warehouse, some in Snowflake or Databricks. Users need a single source of truth for discovery, access requests, and data quality, regardless of where data physically resides.

Modern catalogs sit above both platforms and unify metadata. You maintain one glossary, one set of policies, and one interface for users. As datasets migrate from old to new platforms, the catalog updates lineage automatically. Users searching for “customer churn model” find it regardless of whether it’s still in the legacy warehouse or moved to Snowflake.

This unified view prevents the chaos that typically accompanies cloud migrations. Without it, users maintain bookmarks to both old and new systems, policies diverge between platforms, and governance gaps appear.

Post-migration: Validating and rebuilding trust


After the cutover, business users are skeptical. Does the new platform have all the data? Is quality the same? Can they trust it for decisions? Data catalogs accelerate trust rebuilding through automated quality checks and complete lineage visibility.

Run automated data quality validations: compare row counts between old and new platforms, check for schema drift, and validate that critical data flows are complete. The catalog surfaces these checks in context, so users see quality scores alongside datasets.

Make lineage visible immediately post-migration. When users see complete end-to-end lineage from source systems through transformations to BI dashboards, they trust that nothing got lost. This visibility shortens the “wait and see” period that normally follows migrations.

Migration Phase Without Catalog With Catalog Time Savings Risk Reduction
Pre-migration planning Manual discovery takes weeks Automated profiling in days 40% faster High—no hidden dependencies
Execution Fragmented visibility, broken workflows Unified lineage across hybrid environment 30% faster Medium—governance maintained
Post-migration validation Manual testing takes months Automated quality checks in weeks 50% faster High—trust rebuilt quickly

What are common data catalog migration mistakes to avoid?

Five critical mistakes commonly derail catalog migrations: underestimating metadata complexity, skipping stakeholder engagement, neglecting lineage validation, migrating everything instead of prioritizing, and treating migration as a one‑time event rather than an ongoing governance investment. Each mistake has specific failure modes and remediation approaches.

Mistake 1: Underestimating metadata complexity


Teams assume metadata transfers directly between systems. It doesn’t. Legacy catalogs use proprietary schemas for storing enrichment, and transformation logic is never simple. Custom fields don’t map one-to-one, glossary hierarchies have different structures, and lineage models vary fundamentally between platforms.

This mistake surfaces during execution when you discover that a significant portion of your business metadata has disappeared or transformed incorrectly. By then, you’re too far in to restart. The fix is upfront assessment: export sample metadata before planning, document transformation logic explicitly, and validate transformations on a small dataset before migrating everything.

Mistake 2: IT-only approach


When IT drives catalog migration alone, business users disengage. They don’t see how the new catalog helps them, they’re not trained on new workflows, and they return to informal methods (Slack channels, personal spreadsheets) for finding data. Post-migration adoption collapses.

Successful migrations establish cross-functional steering committees from day one. Include data stewards, domain leads from business units, analytics teams, and compliance representatives. These groups define success criteria, champion adoption within their teams, and ensure the migration serves business needs—not just technical requirements. Organizations with active steering committees often see substantially higher adoption rates.

Mistake 3: No lineage validation


Migrations that skip lineage validation tend to see far more downstream data quality issues in the first 6 months. Broken lineage paths mean you can’t trace data flows, impact analysis fails, and users lose trust when dashboards break with no explanation.

Validate lineage thoroughly post-migration. Trace critical data flows end-to-end (from source systems through transformations to consumption in BI dashboards or ML models). Document any gaps and fix them before declaring migration complete. Lineage is what separates a useful catalog from a glorified data dictionary.

Mistake 4: Migrating everything


Organizations often migrate every asset in their legacy catalog, including deprecated tables, unused reports, and experimental pipelines. This wastes months and bloats the new catalog with irrelevant content.

Apply the 80/20 rule: focus on the 20% of data assets that drive 80% of business value. Use usage statistics from your old catalog to identify high-value datasets. Migrate those first, then reassess whether low-usage assets are worth the effort. Many organizations discover that a meaningful share of cataloged assets, sometimes 30–40%, can be decommissioned rather than migrated.

Mistake 5: Treating migration as one-time project


Teams often view catalog migration as having a start and end date. Migration completes, the project team disbands, and the catalog becomes unmaintained. Metadata becomes stale within months, and you’re back where you started.

Governance requires ongoing investment. Establish data stewardship roles, define ownership for domains, and implement processes for continuous enrichment. Modern catalogs automate much of this (AI-powered description generation, automated lineage updates), but human oversight remains critical. Budget for permanent catalog operations—typically 2-4 FTE depending on organization size.

How do you validate a successful catalog migration?

Validation requires four checks: metadata fidelity (95%+ of enrichment preserved), lineage accuracy (all critical paths traced), user acceptance (20-30% of users actively engaging within the first month), and performance benchmarks (query response times, search relevance). Successful migrations achieve 95%+ metadata fidelity and 80%+ user satisfaction within the first quarter. These metrics confirm the migration succeeded before decommissioning the old catalog.

Metadata fidelity test


Compare enrichment levels between old and new catalogs. For a sample of 100 representative assets, check that descriptions, ownership, glossary terms, and classifications are transferred correctly. Calculate fidelity as: (enriched assets in new catalog / enriched assets in old catalog) × 100.

Target 95%+ fidelity for business-critical metadata. Some loss is acceptable for low-priority assets (experimental tables, deprecated reports), but core production datasets should preserve all enrichment. If fidelity falls below 85%, investigate root causes—transformation logic may be broken.

Lineage accuracy validation


Trace critical data flows end-to-end in the new catalog. Start from source systems (databases, APIs, files), follow transformations (dbt models, Airflow DAGs, stored procedures), and verify connections to consumption points (BI dashboards, ML models). Any gap in lineage paths indicates incomplete migration.

Document test cases for your most important data products. For example: “Customer churn model traces from customer_demographics table through feature engineering pipeline to ML model artifact.” Test each case post-migration and flag gaps immediately. Lineage breaks create trust issues that persist long after migration.

User acceptance testing


Pilot groups (20-30 users representing different roles) test the new catalog for 2-4 weeks. They should perform actual workflows: discover datasets, search for specific tables, trace lineage for impact analysis, and submit access requests. Survey them at the end: Is the new catalog equal to or better than the old one?

Target 80%+ satisfaction. Users will identify friction points (unfamiliar navigation, missing features, slower performance) that need remediation. Address these issues before expanding migration to the full organization. Early adopters become champions if their feedback is incorporated quickly.

Performance benchmarking


Measure search response times, query performance, and page load speeds in the new catalog. Compare baseline measurements from the old catalog. Search should be 2x faster in modern catalogs due to better indexing and caching. If performance is slower, investigate infrastructure issues (undersized clusters, network latency, unoptimized queries).

Run performance tests with realistic loads. If 200 users will query the catalog simultaneously during peak hours, test with 200 concurrent users. Modern catalogs should handle this without degradation. Performance problems post-migration create negative first impressions that hurt adoption.

Success Metric Target How to Measure Red Flag Threshold
Metadata fidelity 95%+ preserved Compare enrichment levels (sample 100 assets) <85% for business-critical assets
Lineage accuracy 100% of critical paths End-to-end trace validation for top 20 data products Any gaps in mission-critical flows
User adoption 20-30% MAU within Q1 Usage analytics (logins, searches, queries) <10% active users after 3 months
Performance 2x faster search Response time benchmarks (median, p95, p99) Slower than legacy system

How Atlan Supports Data Catalog Migrations

Many catalog migrations struggle because organizations underestimate the complexity of preserving business context while re‑ingesting technical metadata. When migrations go wrong, they can miss objectives and generate millions of dollars in rework. Your teams waste weeks manually mapping dependencies between old and new systems. Legacy catalogs like Alation and Collibra have low adoption (often under 20% of data teams), fragmented metadata across multiple tools, and limited automation for lineage tracking during cloud migrations. When you try to migrate platforms or use catalogs to guide broader data transformations, you’re navigating two complex challenges simultaneously.

Atlan’s metadata-first migration approach


As an active metadata platform and metadata control plane, Atlan treats catalog migration as a metadata preservation project, not an infrastructure lift‑and‑shift. We provide dedicated migration playbooks for Alation and Collibra that start with glossary and enrichment export—the business context that takes years to rebuild if lost. Using automated scripts, you export glossary structures, custom fields, READMEs, and ownership mappings from your legacy catalog. This preserves the human-generated context that doesn’t exist in source systems.

From there, Atlan’s native connectors and integrations re‑ingest technical metadata directly from your data platforms (Snowflake, Databricks, BigQuery, BI tools, orchestration systems) in weeks instead of months. You’re not manually recreating table schemas—connectors automate discovery and give you current, accurate technical metadata. This no-code setup means you’re live quickly: HelloFresh onboarded all major sources within weeks, compared to months per connector in their previous catalog.

Atlan’s automated, column-level lineage maintains governance across hybrid environments during cloud migrations. When you’re moving from an on-premises warehouse to Snowflake, Atlan traces data flows across both platforms. You have a unified lineage showing how a BI dashboard pulls from the old warehouse today and will pull from Snowflake tomorrow. This cross-platform visibility supports best practices around maintaining governance during transitions—you’re not fragmenting metadata across systems.

The platform’s active metadata approach automates enrichment continuously. AI‑powered description generation and rule‑based playbooks help teams cut manual metadata work by 60–70%, so your team isn’t recreating business context from scratch. Embedded collaboration tools (Slack, Teams integration) preserve social metadata—user queries, comments, discussions—that drives adoption by showing an active community rather than an empty tool.

Customer migration outcomes


DigiKey migrated from Alation to Atlan in roughly three months with around 95% metadata fidelity, valuing Atlan’s end‑to‑end lineage that Alation couldn’t provide at the scale they needed. The platform’s ability to trace dependencies across their entire data estate accelerated their migration and prevented downstream breaks.

HelloFresh reduced time-to-onboard data sources dramatically: their previous catalog required a month for a single connector, while Atlan’s out-of-the-box integrations had all major sources cataloged within weeks. Before Atlan, internal surveys at HelloFresh showed that a majority of users—over half—found data difficult to discover. Post-migration, automated enrichment and improved search made discovery faster and easier.

P&G used Atlan’s DEV environment to test migration from Alation before PROD cutover, validating metadata transformations and governance workflows in parallel runs. This phased approach—a core best practice—reduced risk and gave stakeholders confidence before full migration.

Atlan’s approach is recognized by analysts as well: the platform is a Leader in both the 2025 Gartner® Magic Quadrant™ for Metadata Management Solutions and the Forrester Wave™: Enterprise Data Catalogs, Q3 2024.

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Frequently Asked Questions About Data Catalog Migration

What is data catalog migration?


Data catalog migration has two meanings: migrating your catalog platform (moving from Alation or Collibra to Atlan, for example) or using a catalog to support broader data migrations like moving to Snowflake or Databricks. Platform migrations take 3-6 months on average and focus on preserving business metadata while re-ingesting technical metadata. Using catalogs for data migrations is an ongoing process that spans 6-18 months and treats the catalog as your control plane during transformations.

How long does a data catalog migration take?


Platform migrations take 3-6 months for medium-sized catalogs with 10,000-100,000 assets, and up to 8 months for large enterprises with over 100,000 assets. The timeline breaks down into assessment (2-4 weeks), planning (4-6 weeks), execution (6-12 weeks), and validation (2-4 weeks). Factors affecting duration include catalog size, custom metadata complexity, and stakeholder alignment. Proper planning reduces timelines by 35% compared to big-bang approaches.

What’s the biggest risk in data catalog migration?


Losing business context (glossary terms, descriptions, ownership) causes the biggest adoption failures post-migration. Technical metadata is straightforward to recreate through connectors, but business and social metadata (comments, queries, custom enrichment) takes years to rebuild if lost. Organizations that preserve 95% or more of business metadata see significantly higher adoption rates. Plan for metadata export and enrichment migration upfront, not as an afterthought.

Should I migrate my catalog or build a new one?


Migrate if you have significant business metadata investment: an enriched glossary, widespread adoption, and active user engagement. Start fresh if your current catalog has low adoption (under 10% monthly active users), minimal enrichment, or outdated technical integrations. Warning: the fresh start temptation often backfires because you lose organizational knowledge. Recommendation: migrate business context while re-ingesting technical metadata through modern connectors. This hybrid approach balances a clean slate with preserved investment.

How do data catalogs help with cloud migration?


Catalogs reduce cloud migration risk through three mechanisms: pre-migration discovery (identify what to move, prioritize based on usage), during-migration governance (maintain access controls across hybrid environments, track lineage), and post-migration validation (automated quality checks, faster trust-rebuilding). Organizations using catalogs often complete cloud migrations faster and with fewer post‑migration data issues because unified metadata, lineage, and quality checks reduce surprises. The catalog becomes your single source of truth during the transition period.

What metadata is most critical to migrate?


Business metadata (glossary terms, descriptions, ownership) and social metadata (user queries, comments, frequently accessed assets) drive adoption, so migrate these first. Technical metadata (schemas, tables) can be re-ingested through connectors rather than exported from the old catalog. Governance metadata (policies, classifications) is compliance-critical. Operational metadata (lineage, quality scores) rebuilds trust. Priority order: governance (legal requirement), business (adoption driver), technical (automation), operational (trust), social (engagement).

Can I run old and new catalogs in parallel?


Yes, and parallel runs reduce risk for large migrations significantly. Run both catalogs for 1-3 months while migrating users incrementally by domain or team. This approach costs more in the short term but reduces failure risk by 60%. Best practice: migrate read-only usage first (discovery, search), then collaborative features (comments, queries), and finally governance workflows (access requests, policies). The parallel approach extends your timeline but prevents catastrophic big-bang failures.

How much does a data catalog migration cost?


Platform migration costs include software licenses for the new catalog (can range from $50,000-$500,000+ annually depending on scale), migration services (consulting or dedicated support, $30,000-$150,000), and internal labor (3-5 full-time employees for 3-6 months, totaling $200,000-$400,000). Budget $300,000-$1 million for medium enterprise migrations. Failed migrations cost 2-3 times more in rework. Using catalogs to support broader data migrations is typically included in catalog ROI through accelerated discovery and reduced migration issues.

What’s Atlan’s approach to catalog migration?


Atlan provides a three-step transition for platform migrations: glossary and enrichment export with automated scripts, asset re-ingestion through 50+ out-of-the-box connectors requiring no custom code, and validation with a dedicated Customer Service Architect guiding the entire process. Timeline: most migrations complete in 3-6 months. Differentiator: DIY no-code setup means you’re live in weeks, and AI-powered enrichment reduces manual metadata work by 70%. Migration resources are available at developer.atlan.com.

How do I choose between catalog vendors?


Evaluate on four criteria: adoption (will both technical and business users actually use it—check G2 reviews for user satisfaction), automation (does it reduce manual metadata work through AI-powered enrichment and automated lineage), extensibility (can it integrate with your full stack through native connectors and API flexibility), and migration support (do they provide dedicated resources like Customer Service Architects). Forrester and Gartner evaluations help benchmark vendors objectively against these criteria.

What should you prioritize in your catalog migration strategy?

Data catalog migrations succeed through metadata-first planning, phased execution, and treating the catalog as both a governance tool during transitions and the destination for consolidated metadata. Remember that migrations without proper planning frequently fail, but phased approaches with catalog support significantly reduce failure rates and can accelerate timelines by roughly a third. In the AI era, unified metadata becomes even more critical as organizations need reliable context for training models and governing AI outputs.

Start with a thorough assessment of your current environment (2-4 weeks), then build an executable transformation plan (4-6 weeks) before beginning phased execution. Validate thoroughly at each stage rather than discovering problems after cutover. Whether you’re migrating your catalog platform or using a catalog to de-risk broader data migrations, the principles remain constant: preserve business context, automate technical ingestion, maintain governance continuity, and measure success through adoption and metadata fidelity.

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Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business's disparate data infrastructure, cataloging and enriching data with business context and security.

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