DataHub vs Select Star.
DataHub and Select Star both anchor in catalog & discovery — 5 dimensions differ, 2 hold. Below: posture, coverage diff, and capability matrix.
Apache-2.0 metadata platform with a serious managed counterpart — strongest event-driven architecture and column-level SQL lineage in OSS.
Automated data catalog with column-level lineage parsed from query logs — being acquired by Snowflake to power Horizon Catalog.
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.
Data teams on Snowflake, BigQuery, Databricks, or Redshift plus dbt and a mainstream BI tool who want a catalog that documents itself — column-level lineage, popularity metrics, ER diagrams, and auto-generated docs parsed from query logs rather than curated by hand.
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.
Snowflake announced a definitive agreement to acquire Select Star's team and platform technology on November 24, 2025 (Snowflake blog; Select Star blog by founder/CEO Shinji Kim). Terms undisclosed. Stated plan: fold Select Star's lineage and discovery into Snowflake's Horizon Catalog to power agentic experiences like Snowflake Intelligence and Cortex Code. Founded 2020 in San Francisco. The public /pricing page has been removed (it now redirects to the homepage).
Each tool's current strategic narrative, verbatim from its profile.
How each tool describes the other.
DataHub's page doesn't directly mention Select Star. See the DataHub detail page.
Select Star cross-shops with atlan, secoda, alation, and collibra among proprietary catalogs, and with datahub, openmetadata, and amundsen on the open-source side — its own site maintains comparison pages against most of these. Its wedge has always been automation-first lineage: where enterprise suites lead with stewardship workflows and OSS catalogs lead with portability, Select Star leads with column-level lineage extracted from query logs with minimal setup, proven at unusual scale (Block used it to automate lineage across an exabyte-scale warehouse). It consumes OpenLineage events, so Airflow and pipeline lineage land in the same graph as warehouse and BI lineage. What it is not is a data-quality engine: there are no monitors, tests, or anomaly detection, which is why the quality-testing cluster scores zero — pair it with a dedicated tool if you need runtime checks.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| DataHub | Select Star | |
|---|---|---|
| Vendor | Acryl Data | Select Star (Snowflake) |
| Deployment | SaaS · Self-hosted | SaaS only |
| License | Open source | Proprietary |
| Pricing | OSS · free | Contact sales |
| Free tier | Yes | No |
| OSS self-host | Yes | No |
| Founded | 2021 | 2020 |
| HQ | Palo Alto, CA | San Francisco, California, United States |
| Status | ● active | ○ acquired |
Full DataHub pricing → Full Select Star pricing →
Both share Primary cluster: Catalog & discovery · dbt integration: Native · OpenLineage: Consumer
Each tool's center of gravity.
| Cluster | DataHub | Select Star |
|---|---|---|
| Quality & testing | 2/3 | 0/3 |
| Lineage & metadata | 3/3 | 2/3 |
| Catalog & discovery | 3/3primary | 3/3primary |
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 | Select Star |
|---|---|---|
| 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
3 of 9 differ| DataHub | Select Star | |
|---|---|---|
| Data contracts | ||
| Access requests | ||
| Free self-host |
Lineage & metadata
1 of 7 differ| DataHub | Select Star | |
|---|---|---|
| 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.
Data teams on Snowflake, BigQuery, Databricks, or Redshift plus dbt and a mainstream BI tool who want a catalog that documents itself — column-level lineage, popularity metrics, ER diagrams, and auto-generated docs parsed from query logs rather than curated by hand. A natural fit for lean-governance organisations that want automated PII tagging with propagation instead of a steward-heavy programme, and for Snowflake-centric shops that read the pending acquisition as a roadmap tailwind: the technology is slated to become part of Horizon Catalog.
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
Select Star stands out for
- Automated column-level lineage parsed from warehouse query logs is the differentiator — proven at very large scale (Block's case study covers lineage automation at exabyte scale)
- Fast time-to-value — auto-generated documentation, popularity/usage metrics, and entity-relationship diagrams appear from metadata without manual curation
- Governance automation — automated PII tagging with tag propagation through lineage, plus RBAC/ABAC policy controls in the Data Access Control module
- Credible AI surface — Ask AI natural-language search, an MCP server exposing metadata and lineage to agents, and automated semantic model generation
Tools both also compete with.
All DataHub alternatives, scored →All Select Star alternatives, scored →
A note on this comparison.
Every capability value above traces to DataHub or Select Star's own structured spec, which links back to its source — nothing here is averaged or smoothed across the two.
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