Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 20:14 UTC

elementary-data / elementary

The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.

HTML · PythonApache-2.0★ 2,410 stars⑂ 226 forkssince Aug 2021View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#data-lineage#data-governance#data-warehouse#snowflake#bigquery#data-analysis#data-pipelines#data-pipeline#lineage#data-reliability#data-observability#dataops#dbt#dbt-packages#analytics-engineer#dbt-artifacts#redshift

elementary-data/elementary holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (62/100). It was last updated today. 3 contributors account for most of its recent work.

98
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.

98
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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

ElementaryOrganization
100 followers21 public repossince Sep 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIelementary-data0.26.01,014,306987 days agodatalineagedata-lineagedata-warehousedwhobservabilitydata-monitoringdata-observabilitysnowflakebigqueryredshiftdata-reliabilityanalytics-engineering

Metrics by category

Vitality

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

94Exceptional · 21% of overall

Development activity

90Excellent
How it's scored
36/36Push recency — last push 0 days ago
26.3/36Commit cadence — 38/52 weeks with commits
18/18Commit volume — 283 commits in the last year
10/10OpenSSF Scorecard: Maintained — 27 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year283
human_commit_share0.87
days_since_last_push0
active_weeks_last_year38

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 ~24.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.26.0
releases_from_tagsno
days_since_latest_release7
mean_days_between_releases24.9
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?

82Excellent · 17% of overall

Popularity & adoption

80Excellent
How it's scored
54.9/60Stars — 2,410 stars
19.6/25Forks — 226 forks
6/15Watchers — 13 watchers
Inputs used
forks226
stars2,410
watchers13
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 1,014,306 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageselementary-data
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1,014,306
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?

81Excellent · 23% of overall

Maintainer resilience (bus factor)

76Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
16.4/22.5Commit distribution — top contributor authored 27% of commits
13.5/13.5Contributor breadth — 90 contributors
10/10OpenSSF Scorecard: Contributors — project has 11 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled90
top_contributor_share0.271

Issue & PR responsiveness

90Excellent
How it's scored
41.5/42Issue resolution — 99% of issues closed
25.3/30PR acceptance — 1,387/1,647 decided PRs merged
8.1/13Newcomer PR acceptance — 5/8 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs1,387
open_issues8
closed_issues684
prs_merged_7d5
prs_decided_7d6
prs_merged_30d26
prs_decided_30d31
issue_closed_ratio0.988
closed_unmerged_prs260
first_time_authors_30d5
first_time_prs_merged_30d5
first_time_prs_decided_30d8

Ownership & stewardship

64Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
14.4/25Owner reach — 100 followers of elementary-data
19.8/25Track record — 21 public repos, account ~5 yr old
Inputs used
followers100
owner_typeOrganization
is_verifiedno
owner_loginelementary-data
public_repos21
account_age_days1,828

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 — 98 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageselementary-data
ecosystemspypi
any_deprecatedno
min_days_since_publish7

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 16 workflow(s)
24/24Tests present
16/16Linter config — .flake8
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 22 out of 22 merged PRs checked by a CI test -- score normalized to 10
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://www.elementary-data.com/
10/10Repository description
10/10Topics — 17 topics
10/10Wiki
Inputs used
topicsdata-lineage, data-governance, data-warehouse, snowflake, bigquery, data-analysis, data-pipelines, data-pipeline, lineage, data-reliability, data-observability, dataops, dbt, dbt-packages, analytics-engineer, dbt-artifacts, redshift
has_wikiyes
homepagehttps://www.elementary-data.com/
docs_sitehttps://www.elementary-data.com/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

78Good · 16% of overall

Security posture

72Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 22 out of 22 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 11 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 27 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4/5SAST — SAST tool is not run on all commits -- score normalized to 8
2/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.2
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages87
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:elementary-data@0.26.0 runtime dependency closure — what installing the published package pulls in — 87 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.

62Moderate · 4% of overall

Agent context & guidance

50Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://www.elementary-data.com/llms.txt)
34.9/40Legible commit history — 57 of 87 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://www.elementary-data.com/llms.txt
legible_history_share0.655
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

64Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — .flake8
11/11Static type checking — elementary/py.typed, mypy.ini
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 39 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configselementary/py.typed, mypy.ini
agent_commit_share0.39
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — HTML with type-check config (elementary/py.typed, mypy.ini)
55/55Manageable file sizes — 0/287 source files over 60KB
Inputs used
primary_languageHTML
largest_source_bytes44,751
source_files_sampled287
oversized_source_files0

Key facts

2,410GitHub stars
90contributors
283commits, last 12 months
0days since last push
100releases
3bus factor
8open 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 ★ / 226 ⇿
0Stars
226Forks
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.

0408012016020024022542021-102024-032026-09
Major 0Minor 23Patch 76

Each point covers 5 days.

OpenSSF Scorecard 7.2 / 10
7.2aggregate

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 · 2026-09-17 20:13 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests22 out of 22 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 11 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained27 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
8SASTSAST tool is not run on all commits -- score normalized to 8
4Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 38
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PyPIdbt-fabric>=1.8,<2.0.0pyproject.toml
PyPIdbt-sqlserver>=1.8,<2.0.0pyproject.toml
All dependencies 244

Full resolved dependency set from the GitHub dependency graph: 76 direct and 168 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPIsnowflake-connector-python4.7.3indirect
PyPIsnowplow-tracker1.1.0indirect
PyPIsortedcontainers2.4.0indirect
PyPIsoupsieve2.9.2indirect
PyPIsqlglot30.18.0indirect
PyPIsqlparams6.2.0indirect
PyPIsqlparse0.6.0indirect
PyPItext-unidecode1.3indirect
PyPIthrift0.24.0indirect
PyPItomlkit0.15.1indirect
PyPItraitlets5.16.1indirect
PyPItrino0.339.0indirect
PyPItypes-decorator—indirect
PyPItypes-google-cloud-ndb—indirect
PyPItypes-jsonschema—indirect
PyPItypes-protobuf—indirect
PyPItypes-python-dateutil—indirect
PyPItypes-pytz—indirect
PyPItypes-pyyaml—indirect
PyPItypes-requests—indirect
PyPItypes-retry—indirect
PyPItypes-s3transfer0.16.0indirect
PyPItypes-setuptools—indirect
PyPItypes-tabulate—indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPItzdata2026.3indirect
PyPItzlocal5.4.4indirect
PyPIwebsockets16.1.1indirect
PyPIzipp4.1.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:elementary-data@0.26.0 pulls in 87 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

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.

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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 v0.34.0 — full methodology · metrics wiki.

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