Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-19 15:35 UTC

databricks / dbt-databricks

A dbt adapter for Databricks.

PythonApache-2.0★ 375 stars⑂ 212 forkssince Oct 2021View on GitHub ↗
KindLibraryCommand-line toolhow this is determined
#dbt#sql#etl#databricks

databricks/dbt-databricks holds a health index of 97 out of 100, placing it in the Exceptional band. It scores highest on Vitality (95/100) and lowest on Security (66/100). It was last updated today. 3 contributors account for most of its recent work.

97
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

97
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 85 is calibrated to 97 on the published index scale (record calibration 2026-08-02).

Ownership

DatabricksOrganization
3,968 followers256 public repossince Jul 2013

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIdbt-databricks1.12.49,096,3041847 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

95Exceptional · 21% of overall

Development activity

91Excellent
How it's scored
36/36Push recency — last push 0 days ago
27/36Commit cadence — 39/52 weeks with commits
18/18Commit volume — 240 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 13 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year240
human_commit_share1
days_since_last_push0
active_weeks_last_year39

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 7 days ago
27/27Release cadence — a release every ~19.3 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv1.12.4
releases_from_tagsno
days_since_latest_release7
mean_days_between_releases19.3
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

79Good · 17% of overall

Popularity & adoption

68Good
How it's scored
41.7/60Stars — 375 stars
19.4/25Forks — 212 forks
7.2/15Watchers — 21 watchers
Inputs used
forks212
stars375
watchers21
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

77Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com
has_pull_request_templateyes

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 9,096,304 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdbt-databricks
dependents—
ecosystemspypi
total_downloads—
monthly_downloads9,096,304
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

89Excellent · 23% of overall

Maintainer resilience (bus factor)

75Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
15.3/22.5Commit distribution — top contributor authored 32% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 12 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled100
top_contributor_share0.322

Issue & PR responsiveness

85Excellent
How it's scored
34.7/42Issue resolution — 83% of issues closed
25.6/30PR acceptance — 884/1,036 decided PRs merged
11.1/13Newcomer PR acceptance — 6/7 first-time contributors' PRs merged in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 27/30 approved changesets -- score normalized to 9
Inputs used
merged_prs884
open_issues98
closed_issues464
prs_merged_7d2
prs_decided_7d2
prs_merged_30d28
prs_decided_30d32
issue_closed_ratio0.826
closed_unmerged_prs152
first_time_authors_30d5
first_time_prs_merged_30d6
first_time_prs_decided_30d7

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
25/25Owner reach — 3,968 followers of databricks
25/25Track record — 256 public repos, account ~13 yr old
Inputs used
followers3,968
owner_typeOrganization
is_verified—
owner_logindatabricks
public_repos256
account_age_days4,786
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 7 days ago
20/20Version history — 184 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdbt-databricks
ecosystemspypi
any_deprecatedno
min_days_since_publish7

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 14 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 27 out of 30 merged PRs checked by a CI test -- score normalized to 9
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://databricks.com
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsdbt, sql, etl, databricks
has_wikiyes
homepagehttps://databricks.com
docs_sitehttps://databricks.com
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

66Good · 16% of overall

Security posture

61Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.2/2.5CI-Tests — 27 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-Review — Found 27/30 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 12 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 13 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
5/5Pinned-Dependencies — all dependencies are pinned
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 27 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate6.1
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

85Excellent
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
10/25Indirect dependencies free of known advisories — 1 affected: sqlparse 0.5.5 (high 7.5)
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories8
affected_packages1
assessed_packages76
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages0
Matched the pypi:dbt-databricks@1.12.4 runtime dependency closure — what installing the published package pulls in — 76 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

68Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes25,278

Verify loop (build / test / typecheck)

60Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — lockfile
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
10/10OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.5/55Manageable file sizes — 3/325 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes88,501
source_files_sampled325
oversized_source_files3

Key facts

375GitHub stars
100contributors
240commits, last 12 months
0days since last push
100releases
3bus factor
98open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 212 ⇿
0Stars
212Forks
99Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0408012016020024020532021-122024-042026-08
Major 0Minor 9Patch 78

Each point covers 5 days.

OpenSSF Scorecard 6.1 / 10
6.1aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-19 15:34 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
9CI-Tests27 out of 30 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 27/30 approved changesets -- score normalized to 9
10Contributorsproject has 12 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 13 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
10Pinned-Dependenciesall dependencies are pinned
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities27 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIclick>=8.2.0, <9.0.0pyproject.toml
PyPIdatabricks-sdk>=0.68.0, <0.118.0pyproject.toml
PyPIdatabricks-sql-connector>=4.1.1, <4.4.1pyproject.toml
PyPIdbt-adapters>=1.22.0, <1.25.0pyproject.toml
PyPIdbt-common>=1.37.0, <1.39.0pyproject.toml
PyPIdbt-core>=1.11.2, <1.12.1pyproject.toml
PyPIdbt-spark>=1.10.0, <1.11.0pyproject.toml
PyPIpackaging>=21.0, <26.0pyproject.toml
PyPIpydantic>=1.10.0, <2.13.0pyproject.toml
All dependencies 139

Full resolved dependency set from the GitHub dependency graph: 18 direct and 121 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIclick8.2.0direct
PyPIclick8.3.1direct
PyPIdatabricks-sdk0.117.0direct
PyPIdatabricks-sdk0.68.0direct
PyPIdatabricks-sql-connector4.1.2direct
PyPIdatabricks-sql-connector4.4.0direct
PyPIdbt-adapters1.22.0direct
PyPIdbt-adapters1.24.5direct
PyPIdbt-common1.37.2direct
PyPIdbt-common1.38.0direct
PyPIdbt-core1.11.2direct
PyPIdbt-core1.12.0direct
PyPIdbt-spark1.10.0direct
PyPIdbt-spark1.10.1direct
PyPIpackaging21.0direct
PyPIpackaging25.0direct
PyPIpydantic1.10.0direct
PyPIpydantic2.12.5direct
PyPIagate1.9.1indirect
PyPIannotated-types0.7.0indirect
PyPIattrs26.1.0indirect
PyPIbabel2.18.0indirect
PyPIcertifi2026.2.25indirect
PyPIcertifi2026.4.22indirect
PyPIcffi2.0.0indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.6indirect
PyPIcharset-normalizer3.4.7indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.14.0indirect
PyPIcryptography46.0.5indirect
PyPIcryptography48.0.0indirect
PyPIdaff1.4.2indirect
PyPIdbt-core-experimental-parser2.0.0a4indirect
PyPIdbt-extractor0.6.0indirect
PyPIdbt-protos1.0.498indirect
PyPIdbt-protos1.0.541indirect
PyPIdbt-semantic-interfaces0.9.0indirect
PyPIdbt-tests-adapter1.20.0indirect
PyPIdebugpy1.8.20indirect
PyPIdeepdiff8.6.2indirect
PyPIdistlib0.4.0indirect
PyPIet-xmlfile2.0.0indirect
PyPIexceptiongroup1.3.1indirect
PyPIexecnet2.1.2indirect
PyPIfilelock3.29.0indirect
PyPIfreezegun1.5.1indirect
PyPIfreezegun1.5.5indirect
PyPIgoogle-auth2.49.1indirect
PyPIgoogle-auth2.52.0indirect
PyPIidentify2.6.19indirect
PyPIidna3.11indirect
PyPIidna3.15indirect
PyPIimportlib-metadata8.8.0indirect
PyPIimportlib-metadata8.9.0indirect
PyPIiniconfig2.3.0indirect
PyPIisodate0.7.2indirect
PyPIjinja23.1.6indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIleather0.4.1indirect
PyPIlibrt0.11.0indirect
PyPIlz44.4.5indirect
PyPImarkupsafe3.0.3indirect
PyPImashumaro3.14indirect
PyPImetricflow0.211.0indirect
PyPImore-itertools10.8.0indirect
PyPImsgpack1.1.2indirect
PyPImypy1.19.1indirect
PyPImypy-extensions1.1.0indirect
PyPInetworkx3.4.2indirect
PyPInetworkx3.6.1indirect
PyPInodeenv1.10.0indirect
PyPInumpy2.2.6indirect
PyPInumpy2.4.3indirect
PyPIoauthlib3.3.1indirect
PyPIopenpyxl3.1.5indirect
PyPIorderly-set5.5.0indirect
PyPIpandas2.2.3indirect
PyPIparsedatetime2.6indirect
PyPIpathspec0.12.1indirect
PyPIplatformdirs4.9.6indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit4.6.0indirect
PyPIprotobuf6.33.6indirect
PyPIpyarrow23.0.1indirect
PyPIpyarrow24.0.0indirect
PyPIpyasn10.6.3indirect
PyPIpyasn1-modules0.4.2indirect
PyPIpybreaker1.4.1indirect
PyPIpycparser3.0indirect
PyPIpydantic-core2.41.5indirect
PyPIpygments2.20.0indirect
PyPIpyjwt2.12.1indirect
PyPIpyparsing3.3.2indirect
PyPIpytest8.3.5indirect
PyPIpytest9.0.3indirect
PyPIpytest-cov6.0.0indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-dotenv0.5.2indirect
PyPIpytest-rerunfailures16.2indirect
PyPIpytest-timeout2.4.0indirect
PyPIpytest-xdist3.6.1indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.3.1indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-slugify8.0.4indirect
PyPIpytimeparse1.1.8indirect
PyPIpytz2026.1.post1indirect
PyPIpytz2026.2indirect
PyPIpyyaml6.0.3indirect
PyPIrapidfuzz3.14.5indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.32.5indirect
PyPIrequests2.34.2indirect
PyPIrpds-py0.30.0indirect
PyPIruff0.14.2indirect
PyPIsix1.17.0indirect
PyPIsnowplow-tracker1.1.0indirect
PyPIsqlglot30.12.0indirect
PyPIsqlparams6.2.0indirect
PyPIsqlparse0.5.4indirect
PyPIsqlparse0.5.5indirect
PyPItabulate0.10.0indirect
PyPItext-unidecode1.3indirect
PyPIthrift0.20.0indirect
PyPIthrift0.24.0indirect
PyPItomli2.4.1indirect
PyPItypes-requests2.33.0.20260513indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.3indirect
PyPItzdata2026.2indirect
PyPIurllib32.6.3indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv21.3.3indirect
PyPIzipp3.23.0indirect
PyPIzipp3.23.1indirect
Dependency advisories 1

Installing pypi:dbt-databricks@1.12.4 pulls in 76 packages, direct and transitive: 1 carry known advisories, of which 0 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
sqlparse0.5.5indirecthigh80.6.0

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.