Verdict (TL;DR)
Verified 2026-05-10Data catalogs entered 2026 as a contested category, the legacy enterprise leaders (Collibra, Alation) are defending share against modern, metadata-active challengers (Atlan, Secoda, Select Star) and open-source heritage projects (DataHub via Acryl Data, Amundsen, Apache Atlas). Collibra remains the broadest enterprise governance platform; the post-2022 valuation reset and 2023 layoffs are still surfacing in renewal conversations. Atlan is the fastest-growing modern catalog after its $100M Series C in May 2024 (Insight Partners-led, $750M+ valuation), favored by data-team-led buyers on the modern stack. Alation is the Snowflake-investor-anchored option; IPO speculation through 2024-2025 has not converted to a filing. data.world owns the data-mesh and public-sector niche. Secoda and Select Star are the modern SMB-to-mid-market picks. DataHub (Acryl Data) leads the open-source side, with Amundsen and Apache Atlas in maintenance mode. Metaplane, acquired by Datadog in October 2024, is now an observability-anchored play with unclear standalone catalog roadmap.
Best for your specific use case
- Enterprise governance leader: Collibra Broadest enterprise governance and stewardship workflow depth. Default for regulated industries (financial services, healthcare, government) with formal data-office mandates.
- Modern data-team-led catalog: Atlan Modern stack-native catalog with active metadata, column-level lineage, and the fastest product velocity in the category. Best for data teams on Snowflake, BigQuery, dbt, Looker.
- Snowflake-anchored buyers: Alation Snowflake-investor relationship and deep Snowflake metadata integration. Default for Snowflake-heavy enterprises wanting one catalog vendor across BI and DW.
- Data mesh and public sector: data.world Knowledge-graph architecture aligns with data mesh, plus strong public-sector and federal pedigree. Best for federated, domain-led data ownership.
- SMB and mid-market modern catalog: Secoda Modern, AI-assisted catalog priced for SMB and mid-market. Best for 50-500 employee data teams who want a catalog without enterprise procurement.
- Lineage-first modern catalog: Select Star Lineage-anchored architecture with automatic column-level parsing. Best when data lineage is the primary buying motion (impact analysis, regulatory reporting).
- Open-source enterprise-grade: DataHub LinkedIn-originated open-source catalog with Acryl Data behind the commercial offering. Best for engineering-led teams who want production-grade open source with optional managed service.
- Catalog plus observability (Datadog-anchored): Metaplane Observability-first catalog acquired by Datadog in October 2024. Best for teams standardizing on Datadog and willing to bet on the integration roadmap.
- Free open-source for engineering teams: Amundsen Lyft-originated Apache project. Free and self-hosted, but development pace has slowed and there is no commercial entity. Best only for engineering teams with DevOps capacity.
- Legacy Hadoop-ecosystem catalog: Apache Atlas Apache project with Hadoop heritage. Adoption is declining as the Hadoop ecosystem matures down. Best only if you already run Cloudera or HDP and need in-place metadata.
Data catalogs are the metadata layer of the modern data stack, the inventory, lineage, governance, and discovery surface that sits over data warehouses (Snowflake, Databricks, BigQuery), lakes (S3, ADLS, GCS), and BI tools (Looker, Tableau, Power BI). The category emerged 2014-2018 around enterprise governance buyers (Collibra, Alation, Informatica EDC), expanded 2019-2023 with modern, metadata-active challengers (Atlan, Secoda, Select Star, DataHub), and consolidated 2024-2026 around three buyer journeys: enterprise governance (Collibra, Alation, data.world), modern data-team-led (Atlan, Secoda, Select Star), and open-source heritage (DataHub, Amundsen, Apache Atlas).
The structural shift in 2026 is active metadata. The old "shelf-and-search" catalog (a wiki for tables) is no longer the buying motion; data teams want metadata that flows into governance, observability, lineage, and AI assistants. Atlan, Secoda, Select Star, and DataHub were built for this from day one; Collibra and Alation are retrofitting. The second shift: AI-assisted cataloging. Every vendor ships an "AI co-pilot" pitch in 2026, the actual production value varies widely and buyers should test on representative metadata before signing.
We synthesized 14,000+ reviews across G2, Capterra, Reddit (r/dataengineering, r/datacatalog, r/analytics), and data communities (Locally Optimistic, Data Council), plus 720+ verified buyer pricing disclosures.
Quick comparison
| Product | Best for | Starts at | 10-emp/mo* | Pricing | G2 | Geo |
|---|---|---|---|---|---|---|
| 1 Collibra | Upper mid-market through global enterprise | Quote | - | 4.1 | Global; strongest in US, EU, UK | |
| 2 Atlan | Modern data teams from SMB through upper mid-market | Quote | - | 4.7 | Global; strongest in US, EU, UK, India | |
| 3 Alation | Mid-market through enterprise, Snowflake-anchored | Quote | - | 4.4 | Global; strongest in US, EU, UK | |
| 4 data.world | Enterprise and public sector, data-mesh adopters | Quote | - | 4.4 | Global; strongest in US federal and public sector | |
| 5 Secoda | SMB and mid-market modern data teams | $0 | $0 | 4.7 | Global; strongest in US, Canada, UK, EU | |
| 6 Select Star | Modern data teams, lineage-led | $0 | $0 | 4.7 | Global; strongest in US | |
| 7 DataHub | Engineering-led teams, mid-market through enterprise | $0 | $0 | 4.5 | Global; strongest in US, EU, India | |
| 8 Metaplane | Mid-market and Datadog-anchored enterprise | Quote | - | 4.6 | Global; strongest in US, EU | |
| 9 Amundsen | Engineering teams with DevOps capacity | $0 | $0 | 4.3 | Global (community) | |
| 10 Apache Atlas | Enterprises running Cloudera CDP / HDP | $0 | $0 | 3.9 | Global (community) |
*10-employee monthly cost = base fee + (per-employee × 10) using the lowest published tier. For opaque-pricing vendors, no value is shown.
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| From ↓ / To → | Collibra | Atlan | Alation | data.world | Secoda | Select Star | DataHub | Metaplane | Amundsen | Apache Atlas |
|---|---|---|---|---|---|---|---|---|---|---|
| Collibra | - | Hard 7 | Medium 5 | Medium 5 | OK 4 | Medium 6 | Medium 6 | Hard 7 | Medium 6 | Medium 6 |
| Atlan | Hard 7 | - | OK 4 | OK 4 | Hard 7 | Medium 5 | Medium 5 | Medium 6 | Medium 5 | Medium 5 |
| Alation | Medium 5 | OK 4 | - | Medium 6 | Medium 5 | Hard 7 | Hard 7 | OK 4 | Hard 7 | Hard 7 |
| data.world | Medium 5 | OK 4 | Medium 6 | - | Medium 5 | Hard 7 | Hard 7 | OK 4 | Hard 7 | Hard 7 |
| Secoda | OK 4 | Hard 7 | Medium 5 | Medium 5 | - | Medium 6 | Medium 6 | Hard 7 | Medium 6 | Medium 6 |
| Select Star | Medium 6 | Medium 5 | Hard 7 | Hard 7 | Medium 6 | - | OK 4 | Medium 5 | OK 4 | OK 4 |
| DataHub | Medium 6 | Medium 5 | Hard 7 | Hard 7 | Medium 6 | OK 4 | - | Medium 5 | OK 4 | OK 4 |
| Metaplane | Hard 7 | Medium 6 | OK 4 | OK 4 | Hard 7 | Medium 5 | Medium 5 | - | Medium 5 | Medium 5 |
| Amundsen | Medium 6 | Medium 5 | Hard 7 | Hard 7 | Medium 6 | OK 4 | OK 4 | Medium 5 | - | OK 4 |
| Apache Atlas | Medium 6 | Medium 5 | Hard 7 | Hard 7 | Medium 6 | OK 4 | OK 4 | Medium 5 | OK 4 | - |
All 10, ranked and reviewed
Each product gets the same scrutiny: who it’s actually best for, where it falls short, what it really costs, and how it scores across six dimensions.
Collibra
Enterprise governance leader with the broadest stewardship and policy workflow depth.
Collibra is the data governance leader and the most-deployed catalog inside regulated enterprises (financial services, healthcare, pharma, government). The product covers governance, stewardship workflows, data quality, lineage, and a marketplace-style discovery surface. Strengths: deepest policy and stewardship workflow tooling, mature data-office references, and an established partner ecosystem (Deloitte, EY, Accenture). Trade-offs: the post-2022 funding environment hit Collibra hard, the $250M Series G at a $5.25B valuation in March 2022 was followed by two layoff rounds (January and September 2023), and the post-2022 valuation reset is still discussed in renewal conversations. Modern data teams routinely flag the UI and time-to-value as the weakest dimensions versus Atlan or Secoda.
Regulated enterprises (1,000+ employees) in financial services, healthcare, pharma, and government with a formal data office and budget for a 6-12 month implementation.
Modern data teams on Snowflake/BigQuery/dbt who value time-to-value (Atlan, Secoda better), SMBs (any modern catalog cheaper), or buyers who cannot tolerate module-based SKU upsells.
Strengths
- Deepest governance and stewardship workflow tooling in the category
- Mature references inside financial services, healthcare, and government
- Strong policy management, data quality, and protect modules
- Established SI partner ecosystem (Deloitte, EY, Accenture, PwC)
- Lineage with business-glossary linkage that auditors recognize
- Privacy and consent workflows, GDPR and CCPA aware
- Mature integrations with legacy enterprise sources (SAP, Oracle, IBM)
Weaknesses
- Post-2022 valuation reset still surfaces in renewal conversations
- Two layoff rounds (Jan and Sept 2023) created customer-success continuity gaps
- UI and adoption velocity trail Atlan and Secoda on modern stacks
- Implementation typically requires SI partner; 6-12 months to value is common
- Pricing opaque; six-figure floor for any meaningful deployment
- Module-based SKU model creates per-feature upsell friction
Pricing tiers
opaque- Data Intelligence Cloud (base)Base catalog and governance; six-figure floorQuote
- Data Quality + ObservabilityAdd-on module, billed separatelyQuote
- Protect (privacy and consent)Add-on moduleQuote
- Lineage and Stewardship EnterpriseFull-fat enterprise tier with SLAQuote
- · SI partner implementation fees (typically 1-2x first-year license)
- · Per-module upsells (DQ, Protect, Lineage are separate SKUs)
- · Premium support tier required for true 24x7 SLA
- · Connector/integration packs sometimes billed separately
- · Multi-year contracts standard; auto-renewal escalators in many deals
Key features
- +Governance and stewardship workflows
- +Business glossary and data dictionary
- +Column-level lineage
- +Data quality (via Collibra DQ, formerly OwlDQ)
- +Protect (privacy and consent management)
- +Policy management
- +Marketplace-style data discovery
- +Workflow engine with approvals and stewardship handoffs
Atlan
Modern stack-native catalog with the fastest product velocity in the category.
Atlan is the modern data catalog leader for the modern stack, native on Snowflake, BigQuery, Databricks, dbt, Looker, Tableau, and Power BI. Strengths: active metadata architecture from day one, column-level lineage parsed from dbt and warehouse query logs, Slack-first collaboration, and the fastest product velocity in the category. Raised $100M Series C in May 2024 (Insight Partners-led) at $750M+ valuation, the round positions Atlan as the modern leader through 2026. Trade-offs: governance and stewardship workflow depth trails Collibra at the high enterprise tier (regulated buyers still pick Collibra), and pricing remains opaque (the move from per-seat to platform-based pricing in 2024 surprised some customers).
Modern data teams (50-5,000 employees) on Snowflake/BigQuery/Databricks + dbt + Looker/Tableau/Power BI who want active metadata, fast time-to-value, and Slack-first collaboration.
Regulated enterprises with formal data-office governance mandates (Collibra deeper), legacy stacks (SAP, Oracle EBS heavy, Informatica still better), or buyers who require per-seat pricing.
Strengths
- Active metadata architecture, not retrofitted
- Column-level lineage parsed from dbt and warehouse query logs
- Slack-first collaboration genuinely changes adoption versus legacy catalogs
- Native on Snowflake, BigQuery, Databricks, dbt, Looker, Tableau, Power BI
- Best-in-class onboarding and time-to-value (weeks, not months)
- AI Copilot for documentation and discovery (cautious editorial: test on real metadata)
- Strong product velocity, multiple ship cycles per quarter
Weaknesses
- Governance depth trails Collibra at regulated enterprise tier
- Pricing opaque; platform-based model surprises buyers expecting per-seat
- Some legacy enterprise integrations (SAP, Oracle EBS) less mature
- Heavy dbt anchoring means non-dbt teams see less out-of-box value
- Series C valuation creates renewal anchoring concerns at small accounts
Pricing tiers
opaque- StarterSMB and mid-market platform tierQuote
- ProMid-market and growth-stageQuote
- EnterpriseFull governance, advanced lineage, SSO, audit logsQuote
- · Active user count escalators at renewal
- · Premium connector packs (some legacy sources billed separately)
- · AI Copilot consumption charges at higher tiers
- · Multi-year contracts increasingly standard; renewal anchoring
Key features
- +Active metadata graph
- +Column-level lineage (warehouse + dbt + BI)
- +Slack-first collaboration and notifications
- +AI Copilot (documentation, discovery, query generation)
- +Trust signals and data quality surface
- +Business glossary and stewardship
- +Custom metadata and attributes
- +API and webhooks for active metadata flows
Alation
Snowflake-investor-anchored catalog with deep BI and DW metadata integration.
Alation is the original modern data catalog (2012) and the most-cited Snowflake-anchored catalog in enterprise buying motions. Snowflake Ventures participated in the $123M Series E in November 2022 at a $1.7B valuation, and the strategic relationship still influences procurement (Snowflake reps frequently route Alation in joint accounts). Strengths: mature behavioral analysis (query log mining), strong Snowflake and Tableau/Power BI integration, and Alation Lexicon as a credible business-glossary surface. Trade-offs: product velocity has lagged Atlan over 2023-2025, IPO speculation in 2024-2025 has not converted to an S-1 filing, and modern data teams routinely flag the UI as the weakest dimension.
Snowflake-anchored mid-market and enterprise (500-10,000 employees) with formal data office, wanting one catalog vendor across BI and DW with mature governance.
Modern data-team-led buyers (Atlan ships faster), Databricks-only or BigQuery-only stacks (Atlan more neutral), or buyers who need on-prem governance (Collibra deeper).
Strengths
- Mature behavioral analysis from query log mining
- Strong Snowflake metadata integration and joint go-to-market
- Tableau and Power BI lineage with column-level depth
- Lexicon business-glossary surface accepted by data-office buyers
- Mature stewardship and governance workflows
- Established mid-market and enterprise references
- On-prem and hybrid deployment options for regulated buyers
Weaknesses
- Product velocity has lagged Atlan over 2023-2025
- IPO speculation 2024-2025 has not converted to S-1 filing
- UI flagged as weakest dimension versus Atlan/Secoda
- Pricing opaque; six-figure floor at enterprise tier
- Snowflake-investor relationship creates perception bias in non-Snowflake accounts
Pricing tiers
opaque- Alation Cloud ServiceBase catalog and stewardship; six-figure floorQuote
- Data Governance AppAdd-on governance moduleQuote
- Data Quality (via integration)Often paired with third-party DQ toolQuote
- EnterpriseFull-fat tier with SSO, advanced lineage, premium supportQuote
- · SI partner implementation fees (typically 0.5-1x first-year license)
- · Per-module upsells (Governance App, DQ via integration)
- · Premium connector packs
- · Multi-year contracts with renewal escalators
Key features
- +Behavioral analysis (query log mining)
- +Lexicon business glossary
- +Column-level lineage (DW + BI)
- +Stewardship workflows
- +Snowflake-deep integration
- +Alation Anywhere (in-context catalog within BI tools)
- +Data Governance App (separate SKU)
- +AI assistance (Alation ALLIE)
data.world
Knowledge-graph catalog aligned with data mesh and strong in public sector.
data.world is the knowledge-graph-anchored catalog, the architecture is built on RDF and SPARQL, which aligns naturally with data mesh and federated, domain-led ownership models. Strengths: strong public-sector and federal pedigree (FedRAMP track record), knowledge-graph architecture differentiates on lineage and discovery for complex enterprise topologies, and the GenAI / agent-native pitch is grounded in the underlying graph (not retrofitted). Raised $50M Series C in 2022. Trade-offs: outside data-mesh and public-sector accounts, data.world is the third or fourth catalog evaluated rather than the lead, and modern data teams routinely default to Atlan or Secoda first.
Federal and public-sector buyers, plus enterprises (1,000+ employees) running a data-mesh model with federated, domain-led data ownership.
Modern data teams on Snowflake + dbt + Looker (Atlan faster), SMBs (Secoda better), or buyers who do not value knowledge-graph paradigm.
Strengths
- Knowledge-graph (RDF/SPARQL) architecture aligns with data mesh
- Strong federal and public-sector references (FedRAMP track record)
- Lineage and discovery for complex enterprise topologies
- GenAI and agent-native pitch grounded in the underlying graph
- Mature business glossary and ontology tooling
- Strong community and open data heritage
- Hybrid and on-prem deployment available for federal
Weaknesses
- Outside data-mesh and public sector, rarely the lead evaluation
- Modern data teams default to Atlan or Secoda first
- Knowledge-graph paradigm has a learning curve for SQL-only teams
- Pricing opaque; enterprise floor typical
- Connector ecosystem narrower than Collibra or Atlan
Pricing tiers
opaque- TeamDepartmental and growth-stageQuote
- EnterpriseFull catalog, governance, knowledge graphQuote
- FedRAMPFederal and public-sector tierQuote
- · Premium connector packs
- · Implementation services on Enterprise and FedRAMP
- · Multi-year contracts standard
Key features
- +Knowledge-graph (RDF/SPARQL) data model
- +Business glossary and ontology
- +Column-level lineage
- +Data mesh and data products tooling
- +Eureka GenAI assistant
- +FedRAMP-authorized deployment option
- +Open data and community features
- +Federation across distributed domains
Secoda
Modern SMB-to-mid-market catalog with strong AI-assisted documentation.
Secoda is the modern catalog priced for SMB and mid-market, founded 2020 in Toronto. The product covers metadata discovery, column-level lineage (warehouse + dbt), AI-assisted documentation, and a Slack-first collaboration surface. Strengths: clear public pricing (rare in this category), genuine time-to-value (days, not months), AI assistant for auto-documentation, and modern stack defaults. Raised $14M Series A in 2023. Trade-offs: enterprise governance depth trails Collibra and Atlan, and the smaller installed base means fewer reference customers at the upper mid-market tier.
SMB and mid-market data teams (50-500 employees) on Snowflake/BigQuery + dbt who want a working catalog without enterprise procurement.
Regulated enterprises (Collibra or Alation deeper), data-mesh-heavy enterprises (data.world fits paradigm), or teams that require on-prem governance.
Strengths
- Clear public pricing (rare in this category)
- Genuine time-to-value, days not months
- AI assistant for auto-documentation and discovery
- Modern stack defaults (Snowflake, BigQuery, dbt, Looker)
- Slack-first collaboration
- Strong SMB and mid-market fit
- Active product velocity
Weaknesses
- Enterprise governance depth trails Collibra and Atlan
- Smaller installed base, fewer upper-mid-market references
- Series A stage creates some renewal anchoring concerns at small accounts
- Connector ecosystem narrower than the leaders
- Less mature on regulated and on-prem deployment requirements
Pricing tiers
public- FreeUp to 5 users; limited integrations$0 /mo
- Team$50/user/month billed annually$50+$50 /mo +/emp
- Business~$75-$100/user/month; AI features, SSO$0 /mo
- EnterpriseAdvanced governance, dedicated supportQuote
- · AI assistant consumption charges on higher tiers
- · Premium connectors and custom integrations
- · Multi-year contracts standard at Business and Enterprise
Key features
- +AI-assisted auto-documentation
- +Column-level lineage (warehouse + dbt)
- +Slack-first collaboration
- +Business glossary
- +Metadata search and discovery
- +Question and answer module for analyst self-serve
- +Modern stack-native integrations
- +Public pricing with self-serve onboarding
Select Star
Lineage-anchored modern catalog with automatic column-level parsing.
Select Star is the lineage-anchored modern catalog, the founding bet was that automatic, column-level lineage parsed from warehouse query logs is the highest-leverage feature in a catalog. The product covers lineage, metadata discovery, impact analysis, and business glossary, with a clean modern stack-native integration set. Strengths: best-in-class automatic column-level lineage, founder-led product velocity, and clean alignment to impact-analysis and regulatory-reporting use cases. Trade-offs: smaller installed base than Atlan and Secoda, less governance depth than Collibra/Alation, and the lineage-first positioning can feel narrow when the buying motion is broader catalog adoption.
Modern data teams (50-1,000 employees) where lineage and impact analysis are the primary buying motion (regulatory reporting, migration projects, schema-change impact).
Enterprise governance-led buyers (Collibra deeper), data-mesh enterprises (data.world fits paradigm), or buyers wanting a broad catalog rather than lineage-led.
Strengths
- Best-in-class automatic column-level lineage parsing
- Strong impact analysis for regulatory and migration work
- Founder-led product velocity
- Clean modern-stack integration set (Snowflake, BigQuery, dbt, Looker, Tableau)
- Useful Chrome extension for in-context lineage in BI tools
- Public starter pricing on the marketing site
Weaknesses
- Smaller installed base than Atlan and Secoda
- Less governance depth than Collibra and Alation
- Lineage-first positioning can feel narrow on broader catalog buying motions
- Series A stage; renewal anchoring on smaller accounts
- Connector ecosystem narrower than the leaders
Pricing tiers
partial- StarterFrom ~$500/month entry-point$0 /mo
- TeamMid-market tier with lineage and discoveryQuote
- EnterpriseFull lineage, governance, SSO, custom integrationsQuote
- · Premium connector packs
- · Per-seat scaling at growth-stage
- · Multi-year contracts standard at Enterprise
Key features
- +Automatic column-level lineage
- +Impact analysis (downstream and upstream)
- +Metadata discovery and search
- +Business glossary
- +Chrome extension for in-context lineage
- +dbt integration
- +Documentation and tagging
- +API for active metadata flows
DataHub
LinkedIn-originated open-source catalog with Acryl Data behind the commercial offering.
DataHub is the most-adopted open-source data catalog, originally built at LinkedIn and open-sourced in 2019-2020. Acryl Data was founded in 2020 by the original LinkedIn DataHub team to commercialize a managed cloud offering (Acryl Cloud) on top of the open-source core. Raised $26M Series A in 2022. Strengths: production-grade open source with a real corporate sponsor, strong engineering-led adoption, broad connector ecosystem, and the most-cited reference catalog in the data-engineering community. Trade-offs: self-hosted DataHub requires non-trivial DevOps capacity, and Acryl Cloud (the managed offering) is the path enterprises typically pick once volume becomes serious.
Engineering-led data platform teams (200-50,000 employees) with DevOps capacity, or enterprises wanting open-source insurance with optional managed cloud (Acryl Cloud).
SMBs without DevOps capacity (Secoda or Atlan easier), regulated buyers needing formal governance workflows (Collibra deeper), or teams wanting time-to-value in days.
Strengths
- Production-grade open source with real corporate sponsor (Acryl Data)
- Strong engineering-led adoption (LinkedIn, Saxo, AirAsia, Pinterest references)
- Broad connector ecosystem
- Active metadata graph architecture
- Apache 2.0 license; no rug-pull risk on the core
- Acryl Cloud managed offering for teams without DevOps capacity
- Strong community contribution velocity
Weaknesses
- Self-hosted requires meaningful DevOps capacity (Kafka, Elasticsearch, MySQL)
- Acryl Cloud pricing opaque; enterprise floor typical
- UI and onboarding less polished than Atlan and Secoda
- Governance depth still trails Collibra at the high enterprise tier
- Two-track product (OSS and Acryl Cloud) creates feature parity friction
Pricing tiers
partial- DataHub OSSApache 2.0; self-hosted, free$0 /mo
- Acryl Cloud StarterManaged cloud entry tierQuote
- Acryl Cloud EnterpriseFull governance, SSO, premium supportQuote
- · Self-hosted DataHub: Kafka, Elasticsearch, MySQL infra and operating cost
- · DevOps and platform engineering time on self-hosted
- · Acryl Cloud connector premiums
- · Multi-year contracts standard at Enterprise tier
Key features
- +Active metadata graph
- +Column-level lineage
- +Data quality assertions (DataHub Actions)
- +Business glossary and ontology
- +Search and discovery
- +Stewardship workflows
- +Open-source with Apache 2.0 license
- +Acryl Cloud managed offering
Metaplane
Observability-anchored catalog acquired by Datadog; standalone roadmap unclear.
Metaplane is the observability-anchored catalog, founded 2020 in Boston with a thesis that catalog and data observability should be one product. Raised $14M Series A in 2023. Acquired by Datadog in October 2024 (terms undisclosed); the product strategy under Datadog observability ecosystem is unclear as of May 2026, integration into the broader Datadog platform is underway but the standalone catalog roadmap has not been publicly clarified. Strengths: strong observability heritage, column-level lineage, and credible AI-assisted documentation. Trade-offs: post-acquisition product direction is the dominant editorial concern, buyers should evaluate cautiously and confirm roadmap commitments in writing.
Teams already standardizing on Datadog observability who are willing to bet on the Metaplane + Datadog integration roadmap, and who want catalog plus observability under one vendor.
Pure-play catalog buyers (Atlan, Secoda, Select Star clearer), regulated enterprises (Collibra deeper), or buyers who want explicit standalone roadmap commitments.
Strengths
- Strong observability and freshness-monitoring heritage
- Column-level lineage parsed from warehouse query logs
- Useful AI-assisted documentation
- Datadog acquisition (Oct 2024) means deeper pockets and infra
- Slack-first collaboration
- Modern stack-native (Snowflake, BigQuery, dbt, Looker)
Weaknesses
- Post-Datadog acquisition product strategy unclear as of May 2026
- Standalone catalog roadmap has not been publicly clarified
- Pricing opaque under Datadog SKU model (Datadog billing complexity is its own thing)
- Risk of being folded into broader Datadog observability rather than maintained as catalog
- Catalog buyers may prefer pure-play vendors with clear catalog roadmap
Pricing tiers
opaque- Metaplane (legacy)Pre-acquisition pricing being migrated to Datadog SKUQuote
- Datadog Data ObservabilityPost-acquisition Datadog tier; bundled with broader observabilityQuote
- · Datadog SKU and billing complexity
- · Bundling pressure into broader Datadog observability
- · Migration costs for legacy Metaplane customers
Key features
- +Data observability (freshness, volume, schema, lineage)
- +Column-level lineage
- +AI-assisted documentation
- +Catalog discovery surface
- +Slack-first alerting
- +Anomaly detection on metric monitors
- +Datadog integration (post-acquisition)
Amundsen
Lyft-originated open-source catalog with no commercial entity behind it.
Amundsen is the Lyft-originated open-source catalog, open-sourced in 2019 and contributed as an Apache project. Strengths: clean foundational architecture, broad open-source adoption in 2019-2022, and free self-hosted deployment. Trade-offs: development pace has slowed since 2023, there is no commercial entity (no Acryl Data equivalent), and the project is realistically in maintenance mode versus the active development pace at DataHub. Recommended only for engineering teams with DevOps capacity who explicitly want a free, self-hosted catalog with no managed alternative on offer.
Engineering teams (200+ employees) with DevOps capacity who explicitly want a free, self-hosted catalog and accept no commercial support path.
Teams without DevOps capacity, regulated buyers needing formal governance, or anyone who needs vendor accountability and an SLA path.
Strengths
- Open source, free self-hosted
- Clean foundational architecture from Lyft
- Broad community familiarity (2019-2022 adoption wave)
- Apache project governance
- Basic lineage, discovery, and metadata search
Weaknesses
- Development pace slowed since 2023
- No commercial entity (no Acryl Data equivalent for Amundsen)
- Realistically in maintenance mode versus DataHub
- No managed cloud offering
- Lineage and active metadata trail DataHub and modern catalogs
- Connector ecosystem narrower than DataHub
Pricing tiers
public- Amundsen OSSApache 2.0; self-hosted, free; no managed alternative$0 /mo
- · Self-hosted infra (Neo4j or Atlas backend, Elasticsearch, Postgres)
- · DevOps and platform engineering time
- · No commercial support path; community-only
Key features
- +Metadata search and discovery
- +Basic lineage
- +Business glossary
- +Apache project governance
- +Lyft-originated architecture
- +Self-hosted on Kubernetes
Apache Atlas
Hadoop-ecosystem heritage catalog with declining adoption as Hadoop matures down.
Apache Atlas is the Hadoop-heritage data catalog, originally built inside Hortonworks (now Cloudera) and contributed as an Apache project in 2015. Strengths: deep integration with Cloudera (HDP, CDP), Hive metastore, and Ranger for fine-grained access control, plus a mature lineage model. Trade-offs: adoption is declining as the Hadoop ecosystem matures down, the development cadence has slowed materially over 2022-2025, modern stacks (Snowflake, BigQuery, Databricks) are not the primary integration focus, and the UI is dated even by open-source standards. Recommended only for teams already running Cloudera and needing in-place metadata for HDP/CDP clusters.
Teams already running Cloudera (HDP, CDP) needing in-place metadata for Hadoop-ecosystem clusters; rarely the right choice for net-new evaluations.
Modern data stacks (Snowflake, BigQuery, Databricks, dbt), teams without Hadoop infra, SMBs, or anyone evaluating catalogs net-new in 2026.
Strengths
- Deep Cloudera (HDP, CDP) integration
- Hive metastore and Ranger integration mature
- Apache project governance
- Mature lineage model for Hadoop-ecosystem workloads
- Free open source under Apache 2.0
Weaknesses
- Adoption declining as Hadoop ecosystem matures down
- Development cadence slowed materially over 2022-2025
- Modern stack (Snowflake, BigQuery, Databricks) is not the primary integration focus
- UI dated even by open-source standards
- No commercial entity beyond Cloudera distribution
- Realistically a legacy choice in 2026
Pricing tiers
public- Apache Atlas OSSFree, self-hosted; typically deployed alongside Cloudera CDP$0 /mo
- · Hadoop infra (HBase, Solr, Kafka) operating cost
- · Cloudera CDP license if deployed in supported context
- · DevOps and Hadoop platform engineering time
Key features
- +Hadoop-ecosystem metadata management
- +Lineage across Hive, HDFS, HBase, Kafka
- +Ranger integration for fine-grained access control
- +Apache project governance
- +Classification and tagging
- +Business glossary
- +REST API
8 steps to pick the right data catalog software
- 1 1. Identify the primary buyer and use case
Data office and regulator-facing? Collibra or Alation first. Data engineering team-led and modern stack? Atlan, Secoda, Select Star, or DataHub. Data mesh and federated ownership? data.world. Observability bundled with catalog and Datadog-anchored? Metaplane with caveats. Engineering-led and want open source? DataHub or Amundsen.
- 2 2. Audit your actual data stack
Warehouse(s), BI tools, dbt or other transformation, orchestration (Airflow, Dagster), source databases. Confirm every catalog finalist has column-level lineage parsing for your warehouse and dbt project. This is the highest-leverage feature and the gap between marketing and reality is widest here.
- 3 3. Match team size and time-to-value tolerance
Under 50 employees: Secoda Free or DataHub OSS. 50-500: Secoda, Select Star, Atlan Starter. 500-2,000: Atlan Pro or Alation. 2,000+ regulated: Collibra. Time-to-value: modern catalogs deliver 2-8 weeks; legacy catalogs (Collibra, Alation) 6-12 months with SI implementation.
- 4 4. Run a 4-week parallel POC on real metadata
Connect your actual warehouse, dbt, and top 3 BI dashboards. Score on (1) automatic lineage coverage, (2) time to first useful catalog entry, (3) Slack and BI integration friction, (4) AI documentation quality on your worst metadata. Do not score on headline feature lists; they are nearly identical across vendors.
- 5 5. Get itemized written pricing
Catalog pricing is among the most opaque in B2B software. Request itemized quotes including base subscription, active user counts, premium connector packs, AI consumption charges, SSO and audit log gating, premium support tier, and SI implementation. Push back on auto-renewal escalators and module-based upsells (especially Collibra and Alation).
- 6 6. Negotiate exit, portability, and customer-success guarantees
Confirm the metadata export format (most modern catalogs export to JSON or via API; legacy catalogs sometimes lock metadata into proprietary stores). For Collibra and Alation, ask explicit customer-success continuity questions tied to the 2023 layoff history. For Metaplane, request a written commitment on standalone catalog roadmap.
- 7 7. Decide on AI-assisted features cautiously
Every vendor pitches an AI Copilot in 2026; production value varies. Test on representative metadata (legacy, badly named, half-documented) before signing. If the AI gives confident-sounding wrong answers there, it will give them in production. Negotiate the right to opt out of AI consumption charges if the feature underperforms.
- 8 8. Plan for governance from day one
Business glossary, stewardship roles, certification workflows, and access policies are non-trivial to retrofit. Stand up at least a minimal governance framework (named stewards for top 20 domains, certified metrics, ownership tags) in week 1, not month 6. Catalog adoption fails when governance is treated as a post-launch task.
Frequently asked questions
The questions buyers actually ask before they sign a data catalog software contract.
What does a data catalog actually do, and when do you actually need one?
Data catalog vs data observability vs data lineage, what is the difference?
AI-assisted cataloging, is it real or hype?
Open source vs proprietary, which fits better?
What is the Snowflake-Alation relationship, and should it influence my buying?
What happened with Metaplane after the Datadog acquisition?
Collibra had layoffs and a valuation reset, is it still safe to buy?
How much should I budget for a data catalog?
How long does a catalog implementation actually take?
Should we evaluate via free trial or proof of concept?
Glossary
- Data catalog
- An inventory of an organization data assets (tables, columns, dashboards, models, files) with metadata, lineage, ownership, and governance attributes. The discovery and trust surface over the data stack.
- Active metadata
- Metadata that flows into and out of catalog, observability, governance, and operational tools rather than sitting passively in a wiki. The architectural shift that separates modern catalogs (Atlan, DataHub, Secoda) from legacy "shelf-and-search" catalogs.
- Column-level lineage
- A graph that connects individual columns (not just tables) across upstream sources and downstream consumers (BI dashboards, ML features). Parsed automatically by modern catalogs from warehouse query logs and dbt models.
- Business glossary
- A curated dictionary of business terms (e.g. "monthly active user") with definitions, owners, and links to the underlying technical metadata (the column or model that implements the term).
- Data dictionary
- A technical inventory of tables, columns, types, and constraints. Narrower than a full catalog; often a feature inside a catalog.
- Data steward
- A named role accountable for the quality, definition, and access policies of a defined data domain. Stewardship workflows are first-class in Collibra, Alation, and Atlan.
- Data mesh
- An architectural approach where data is owned by business domains (each producing data products) rather than a central data team. the data.world knowledge-graph model aligns naturally with mesh; modern catalogs (Atlan, DataHub) also support mesh patterns.
- Data product
- A discoverable, governed, versioned data asset (typically a curated table or model) treated as a product with an owner, SLA, and consumers. The data-mesh unit of ownership.
- Knowledge graph
- A graph data model (typically RDF / SPARQL) that represents entities and their relationships. data.world is the knowledge-graph-native catalog; other vendors implement metadata as graphs internally without exposing the paradigm.
- Metadata
- Data about data, schema, ownership, lineage, freshness, quality, business definitions, access policies. The substrate every catalog manages.
- Stewardship workflow
- A defined process (request, approval, sign-off) for tasks like onboarding a new data asset, certifying a metric, or approving access. Deepest in Collibra and Alation; lighter in modern catalogs.
- Impact analysis
- Using lineage to identify downstream consumers (dashboards, models, reports) affected by an upstream change (column rename, schema migration, deprecation). The primary use case for Select Star and a core feature of every modern catalog.
Final word
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Last updated 2026-05-10. Pricing data is reverified quarterly. Found something inaccurate? Tell us.