Data Stack Index / v 02.06
Verified 2026·05·30
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Compare Same primary cluster · Catalog & discovery

Collibra vs DataHub.

Collibra and DataHub both anchor in catalog & discovery — 9 dimensions differ, 3 hold. Below: posture, coverage diff, and capability matrix.

Same Sales-ledCatalog & discovery (primary)ML anomaly detection
Differ on DeploymentLicenseFree tierOSS optiondbt depthdbt-nativeAuthoring styleMonitor surfaceWarehouse coverage
01
Strategic posture

What each is betting on.

● Collibra

Independent and active as of mid-2026. Founded 2008 in Brussels by VUB researchers; one of the original category-defining governance incumbents. Itself an acquirer, not a target — Raito (access management), Husprey (SQL notebook), and Deasy Labs (unstructured/AI metadata) in 2025, on top of OwlDQ (2021, now the Data Quality & Observability module). Last disclosed private valuation USD 5.25B (2021).

● DataHub

DataHub originated at LinkedIn (open-sourced February 2020); Acryl Data was founded 2021 by ex-LinkedIn engineers to build the managed product. Series A $21M (2022, 8VC); Series B $35M (2024, Bessemer). 2024–2025 rebrand consolidated the OSS and managed offerings under a single 'DataHub' brand, with 'DataHub Cloud' replacing the older 'Acryl Cloud' name.

Each tool's current strategic narrative, verbatim from its profile.

02
Head-to-head

How each tool describes the other.

● Collibra on DataHub

Collibra is the heavyweight governance incumbent, most directly cross-shopped with atlan (modern, UX-led, lower TCO), alation, and the OSS catalogs datahub and openmetadata (open, engineer-led, free self-host). It typically wins where formal governance, regulatory auditability, and single-vendor breadth outweigh developer ergonomics and price. Its data-quality module competes with monte-carlo, anomalo, bigeye, and soda, though those remain better for CI/pipeline-gating and dbt-native workflows.

● DataHub on Collibra

DataHub's page doesn't directly mention Collibra. See the DataHub detail page.

Each quote is pulled from the named tool's own "Where it fits" write-up.

03
At a glance

Spec sheet diff.

Collibra DataHub
Vendor Collibra Acryl Data
Deployment SaaS only SaaS · Self-hosted
License Proprietary Open source
Pricing Contact sales OSS · free
Free tier No Yes
OSS self-host No Yes
dbt integration Plugin Native
Founded 2008 2021
HQ Brussels, Belgium Palo Alto, CA
Authoring style SQL YAML
Test paradigm Assertion + anomaly Assertion-based

Both share Primary cluster: Catalog & discovery · OpenLineage: Consumer · Status: ● active

04
Cluster strength

Each tool's center of gravity.

Cluster Collibra DataHub
Quality & testing 2/3 2/3
Catalog & discovery 3/3primary 3/3primary
Lineage & metadata 3/3 3/3

Scored 0–3 per cluster on the same rubric across all tools. A 0 means the cluster isn't the tool's focus, not that the feature is absent. See the methodology.

05
Coverage

Where they cover different ground.

Target personas
Both Data engineer · Data steward · Governance lead
Only Collibra CDO
Only DataHub Analytics engineer · Platform engineer
Company size fit
Both Enterprise · Mid-market
Only DataHub Scaleup
Warehouse coverage
Both BigQuery · Databricks · Fabric · MSSQL · Postgres · Redshift · Snowflake · Synapse
Only DataHub Athena · ClickHouse · MySQL · Trino
Orchestrators
Both Airflow · dbt Cloud
Only DataHub Airbyte · Dagster · Fivetran · Flink · Prefect · Spark · dbt Core
Monitor surface
Both Warehouse column · Warehouse table
Only DataHub dbt model
Alerting channels
Both Email
Only DataHub PagerDuty · Slack · Webhook
06
Declared features

The declared feature set.

7 of 10 declared features differ — listed first. These are each tool's self-declared key_features; a blank dot means undeclared, not impossible.

Feature Collibra DataHub
ML Anomaly Detection Quality & testing
Schema Change Detection Quality & testing
PII Auto-Classification Catalog & discovery
OpenLineage-Native Lineage & metadata
Reverse Impact Analysis Lineage & metadata
Table-Level Lineage Lineage & metadata
Transformation Lineage Lineage & metadata
Data Contracts Quality & testing
Business Glossary Catalog & discovery
Column-Level Lineage Lineage & metadata
07
Capability matrix

Where they disagree.

Quality & testing

1 of 13 differ
Collibra DataHub
dbt-native
Both also haveML anomaly detection · Schema drift · Freshness · Volume · Custom SQL · Data contracts · Incident management · Root-cause UI · Column profiling
Neither doesPre-merge diffing · Circuit breaker · CI / CLI runs

Catalog & discovery

1 of 9 differ
Collibra DataHub
Free self-host
Both also haveBusiness glossary · NL search · Data contracts · Governance flows · Access requests · PII auto-classify · Tag propagation · Ownership tracking

Lineage & metadata

0 of 7 differ

No disagreement on any of the 7 capabilities in this cluster — they match across the board.

Both also haveColumn-level · Cross-system · Reverse impact · Historical · BI lineage · Lineage API
Neither doesLineage diff
08
Verdict

When to pick each.

● Pick Collibra if

Large, regulated enterprises — banks, insurers, pharma, public sector — that need a governance-first control plane: a real CDO function, formal stewardship, a business glossary, policy enforcement, and auditable lineage for regulations like BCBS 239, GDPR, SOX, HIPAA, and the EU AI Act. Collibra is strongest where governance process and accountability matter more than developer ergonomics, and where a single vendor for catalog plus governance plus lineage plus data quality plus AI governance is preferred over best-of-breed point tools.

● Pick DataHub if

Engineering-led data platforms that want an open, extensible metadata layer they can shape to their stack — with a credible managed escape hatch (DataHub Cloud) when self-hosting Kafka, Elasticsearch, and the graph store stops being fun. Particularly strong for organisations that already think in events: DataHub's Kafka-based Metadata Change Log makes it a natural fit for shops that want metadata to flow the same way data does. The SQL parser is genuinely best-in-class in the OSS catalog space, with SQLGlot-based column-level lineage benchmarked at 97–99% accuracy on standard corpora — materially better than competing parsers. A good fit also for teams wiring DataHub into AI agents via the native MCP server.

09
Strengths

What each does best.

Collibra stands out for

  • [+] The deepest governance and stewardship tooling in the cluster — a configurable workflow engine, business glossary, policies, ownership, and audit trails purpose-built for regulated enterprises
  • [+] Broad single-vendor footprint — catalog, lineage (table and column, OpenLineage-aware), an ML data-quality module (from the OwlDQ acquisition), privacy, and AI governance under one platform
  • [+] Strong automated lineage with root-cause and downstream impact analysis at table, column, and report level, with in-line transformation context
  • [+] A mature, analyst-recognised leader with 100+ catalog integrations and a large regulated-enterprise customer base

DataHub stands out for

  • [+] Best-in-class column-level SQL lineage parser (SQLGlot-based, benchmarked at 97–99% accuracy on standard corpora)
  • [+] Event-driven Kafka MCL architecture — metadata changes are a stream, not a snapshot, which composes well with downstream consumers
  • [+] Native OpenLineage consumer endpoint plus dedicated Spark and Airflow plugins
  • [+] Open-core model with a credible managed product (DataHub Cloud) means buyers can start free and graduate without a re-platforming
10
Other alternatives

Tools both also compete with.

A note on this comparison.

Every capability value above traces to Collibra or DataHub's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.

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