Amundsen vs DataHub.
Amundsen and DataHub both anchor in catalog & discovery — 5 dimensions differ, 4 hold. Below: posture, coverage diff, and capability matrix.
The Lyft-born OSS catalog that invented search-first discovery — historically important, but development has largely stalled since 2024.
Apache-2.0 metadata platform with a serious managed counterpart — strongest event-driven architecture and column-level SQL lineage in OSS.
Teams that already run Amundsen and need to understand what they have, or teams with a genuinely minimal requirement — usage-ranked table search and ownership tracking, nothing more — who are comfortable owning a codebase that is no longer moving.
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.
What each is betting on.
Honest read: maintenance mode. Created at Lyft, open-sourced October 2019, joined LF AI & Data as an incubation project in August 2020. Development has slowed to near-zero — the last release was databuilder 7.5.1 (August 2024) and the last commit to the monorepo (April 2025) moved a maintainer to emeritus status. Stemma, the managed-Amundsen company founded by an Amundsen co-creator, was acquired by Teradata in 2023 and discontinued as a standalone product. The repo is not archived, but treat this as software that is no longer evolving.
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.
How each tool describes the other.
Against datahub and openmetadata, the comparison is mostly historical: both cover Amundsen's discovery core, then add business glossary, governance workflows, column-level lineage, and data quality — with active communities shipping monthly. Amundsen's remaining edge is smallness: if your entire requirement is usage-ranked table search with owners and column stats, Amundsen does that with less conceptual overhead than either successor. Against apache-atlas, Amundsen is the friendlier UX but the less-maintained codebase; Atlas persists inside Hadoop-legacy estates Amundsen never targeted. Against select-star or secoda, the trade is self-hosted OSS versus paying for a maintained SaaS that does the same search-first job.
DataHub's page doesn't directly mention Amundsen. See the DataHub detail page.
Each quote is pulled from the named tool's own "Where it fits" write-up.
Spec sheet diff.
| Amundsen | DataHub | |
|---|---|---|
| Vendor | LF AI & Data Foundation | Acryl Data |
| Deployment | Self-hosted only | SaaS · Self-hosted |
| Pricing | OSS · paid tiers | OSS · free |
| dbt integration | Plugin | Native |
| Founded | 2019 | 2021 |
| HQ | — | Palo Alto, CA |
Full Amundsen pricing → Full DataHub pricing →
Both share Primary cluster: Catalog & discovery · License: Open source · Free tier: Yes · OSS self-host: Yes · OpenLineage: Consumer · Status: ● active
Each tool's center of gravity.
| Cluster | Amundsen | DataHub |
|---|---|---|
| Quality & testing | 0/3 | 2/3 |
| Lineage & metadata | 1/3 | 3/3 |
| 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.
5 of 6 declared features differ — listed first.
These are each tool's self-declared key_features; a blank dot means
undeclared, not impossible.
| Feature | Amundsen | DataHub |
|---|---|---|
| Data Contracts Quality & testing | ||
| Schema Change Detection Quality & testing | ||
| Business Glossary Catalog & discovery | ||
| Column-Level Lineage Lineage & metadata | ||
| OpenLineage-Native Lineage & metadata | ||
| Table-Level Lineage Lineage & metadata |
Where they disagree.
Catalog & discovery
7 of 9 differ| Amundsen | DataHub | |
|---|---|---|
| Business glossary | ||
| NL search | ||
| Data contracts | ||
| Governance flows | ||
| Access requests | ||
| PII auto-classify | ||
| Tag propagation |
Lineage & metadata
4 of 7 differ| Amundsen | DataHub | |
|---|---|---|
| Column-level | ||
| Cross-system | ||
| Reverse impact | ||
| Historical |
When to pick each.
Teams that already run Amundsen and need to understand what they have, or teams with a genuinely minimal requirement — usage-ranked table search and ownership tracking, nothing more — who are comfortable owning a codebase that is no longer moving. The core idea still holds up: index tables, dashboards, and people into Elasticsearch, rank results by query-log usage so the tables analysts actually trust float to the top, and keep the UX focused on the single question "which table should I use?" Databuilder's pull model is plain Python, so extending it from an existing Airflow deployment is straightforward for a data platform team.
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.
What each does best.
Amundsen stands out for
- Pioneered usage-ranked, search-first discovery — PageRank-style ranking from query logs remains a genuinely good idea that successors copied
- Deliberately small surface area — analysts get table search, owners, column stats, and previews without a governance platform's learning curve
- Apache-2.0 under neutral LF AI & Data governance, with nothing held back for a paid tier
- Databuilder is plain-Python ETL — custom extractors are easy to write and schedule from an existing Airflow deployment
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
Tools both also compete with.
All Amundsen alternatives, scored →All DataHub alternatives, scored →
A note on this comparison.
Every capability value above traces to Amundsen 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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