Data Stack Index / v 02.06
Verified 2026·07·03
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Compare Same primary cluster · Catalog & discovery

Amundsen vs Select Star.

Amundsen and Select Star both anchor in catalog & discovery — 7 dimensions differ, 1 hold. Below: posture, coverage diff, and capability matrix.

Same Catalog & discovery (primary)
Differ on DeploymentLicensePricing transparencyFree tierOSS optiondbt depthWarehouse coverage
1 ● Amundsen leads
3 shared
8 Select Star leads ○
● Amundsen

The Lyft-born OSS catalog that invented search-first discovery — historically important, but development has largely stalled since 2024.

○ Select Star

Automated data catalog with column-level lineage parsed from query logs — being acquired by Snowflake to power Horizon Catalog.

● Pick Amundsen if

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.

○ Pick Select Star if

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.

01
Strategic posture

What each is betting on.

● Amundsen

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.

● Select Star

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.

02
Head-to-head

How each tool describes the other.

● Amundsen on Select Star

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.

● Select Star on Amundsen

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.

03
At a glance

Spec sheet diff.

Amundsen Select Star
Vendor LF AI & Data Foundation Select Star (Snowflake)
Deployment Self-hosted only SaaS only
License Open source Proprietary
Pricing OSS · paid tiers Contact sales
Free tier Yes No
OSS self-host Yes No
dbt integration Plugin Native
Founded 2019 2020
HQ San Francisco, California, United States
Status ● active ○ acquired

Full Amundsen pricing → Full Select Star pricing →

Both share Primary cluster: Catalog & discovery · OpenLineage: Consumer

04
Cluster strength

Each tool's center of gravity.

Cluster Amundsen Select Star
Lineage & metadata 1/3 2/3
Quality & testing 0/3 0/3
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.

05
Coverage

Where they cover different ground.

Target personas
Both Analyst · Data engineer
Only Amundsen Platform engineer
Only Select Star Analytics engineer · Data steward · Governance lead
Company size fit
Both Mid-market · Scaleup
Only Select Star Enterprise
Warehouse coverage
Both BigQuery · Databricks · MSSQL · MySQL · Postgres · Redshift · Snowflake
Only Amundsen Athena · Trino
Orchestrators
Both Airflow · dbt Core
Only Select Star Aws Glue · Fivetran · dbt Cloud
06
Declared features

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 Select Star
Business Glossary Catalog & discovery
PII Auto-Classification Catalog & discovery
Column-Level Lineage Lineage & metadata
Reverse Impact Analysis Lineage & metadata
Transformation Lineage Lineage & metadata
Table-Level Lineage Lineage & metadata
07
Capability matrix

Where they disagree.

Catalog & discovery

6 of 9 differ
Amundsen Select Star
Business glossary
NL search
Governance flows
PII auto-classify
Tag propagation
Free self-host
Both also haveOwnership tracking
Neither doesData contracts · Access requests

Lineage & metadata

3 of 7 differ
Amundsen Select Star
Column-level
Cross-system
Reverse impact
Both also haveBI lineage · Lineage API
Neither doesHistorical · Lineage diff
08
Verdict

When to pick each.

● Pick Amundsen if

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.

○ Pick Select Star if

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.

09
Strengths

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

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
10
Other alternatives

Tools both also compete with.

All Amundsen alternatives, scored →All Select Star alternatives, scored →

A note on this comparison.

Every capability value above traces to Amundsen 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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