DataHub vs Secoda.
DataHub and Secoda both anchor in catalog & discovery — 5 dimensions differ, 3 hold. Below: posture, coverage diff, and capability matrix.
What each is betting on.
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.
Acquired by Atlassian; announced via Secoda's blog (Dec 4, 2025) and reported by TechTarget (Dec 5, 2025). Terms undisclosed. Atlassian plans to fold Secoda's semantic cataloging into its Teamwork Graph / Rovo AI and migrate it onto the Atlassian Cloud Platform over time. As of mid-2026 Secoda still operates under its own brand with the founding team aboard; near-term customer experience is said to be unchanged. Founded 2021 in Toronto (Y Combinator); ~USD 14M Series A in 2023.
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
DataHub's page doesn't directly mention Secoda. See the DataHub detail page.
Secoda cross-shops most directly with atlan, datahub, and openmetadata as a catalog/discovery plane, and with unity-catalog for teams already on Databricks. Against Atlan it positions as faster-to-value, lighter-weight, and more self-serve for mid-market teams; against the OSS catalogs it trades open-source portability for a polished AI-first UX and bundled observability. It is not a dedicated data-quality engine like monte-carlo, anomalo, or soda — its monitoring is consolidated and metadata-driven rather than deep test authoring, which is why we score the quality-testing cluster zero.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| DataHub | Secoda | |
|---|---|---|
| Vendor | Acryl Data | Secoda (Atlassian) |
| License | Open source | Proprietary |
| Pricing | OSS · free | Contact sales |
| Free tier | Yes | No |
| OSS self-host | Yes | No |
| OpenLineage | Consumer | None |
| HQ | Palo Alto, CA | Toronto, Ontario, Canada |
| Status | ● active | ○ acquired |
Both share Primary cluster: Catalog & discovery · Deployment: SaaS · Self-hosted · dbt integration: Native · Founded: 2021
Each tool's center of gravity.
| Cluster | DataHub | Secoda |
|---|---|---|
| Quality & testing | 2/3 | 0/3 |
| 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.
Where they cover different ground.
The declared feature set.
6 of 9 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | DataHub | Secoda |
|---|---|---|
| Data Contracts Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| PII Auto-Classification Catalog & discovery | ||
| OpenLineage-Native Lineage & metadata | ||
| Reverse Impact Analysis Lineage & metadata | ||
| Transformation Lineage Lineage & metadata | ||
| Business Glossary Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
2 of 9 differ| DataHub | Secoda | |
|---|---|---|
| Data contracts | ||
| Free self-host |
Lineage & metadata
1 of 7 differ| DataHub | Secoda | |
|---|---|---|
| Historical |
When to pick each.
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.
Mid-market and scaleup data teams that want one AI-native tool covering catalog, search, lineage, documentation, and basic observability rather than running separate catalog, lineage, and monitoring tools — especially teams that value a natural-language assistant for self-serve data questions and broad business-user adoption. A strong fit for organisations on Snowflake, BigQuery, or Databricks plus dbt and a modern BI tool who want fast time-to-value and lighter governance overhead than enterprise suites like Atlan or Collibra.
What each does best.
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
Secoda stands out for
- AI-native search and assistant as the primary interface — natural-language data questions across the catalog, plus purpose-built agents for search, documentation, observability, and governance
- Consolidated — catalog, data dictionary/glossary, column- and table-level lineage, governance, and no-code monitoring in one workspace
- Strong automated lineage including column-level, BI-tool coverage, impact analysis, and downstream/upstream owner notifications
- Fast time-to-value and broad business-user adoption relative to heavyweight enterprise catalogs, with 50+ no-code connectors
Tools both also compete with.
A note on this comparison.
Every capability value above traces to DataHub or Secoda's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
Notice something inaccurate? Send a correction.