Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 17:04 UTC

adidas / lakehouse-engine

The Lakehouse Engine is a configuration driven Spark framework, written in Python, serving as a scalable and distributed engine for several lakehouse algorithms, data flows and utilities for Data Products.

PythonApache-2.0★ 293 stars⑂ 54 forkssince Nov 2022View on GitHub ↗

adidas/lakehouse-engine holds a health index of 60 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (74/100) and lowest on Security (17/100). It was last updated 62 days ago. A single contributor accounts for most of its recent work.

60
overall / 100
Moderate

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.

60
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 57 is calibrated to 60 on the published index scale (record calibration 2026-08-02).

Ownership

adidasOrganization · verified domain
221 followers34 public repossince Jan 2018

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

Package ecosystems

Metrics by category

Vitality

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

54Moderate · 21% of overall
How it's scored
18/36Push recency — last push 62 days ago
2.1/36Commit cadence — 3/52 weeks with commits
6.3/18Commit volume — 4 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year4
human_commit_share1
days_since_last_push62
active_weeks_last_year3
How it's scored
27/27Ships releases — 21 releases published
36/36Release recency — latest release 62 days ago
19.8/27Release cadence — a release every ~62.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count21
latest_release_tagv2.2.0
releases_from_tagsno
days_since_latest_release62
mean_days_between_releases62.1

Community & Adoption

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

66Good · 17% of overall
How it's scored
40/60Stars — 293 stars
14.4/25Forks — 54 forks
6.7/15Watchers — 17 watchers
Inputs used
forks54
stars293
watchers17
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
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_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateyes
How it's scored
47.9/80Monthly downloads — 3,910 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageslakehouse-engine
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,910
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?

64Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
28/42Issue resolution — 67% of issues closed
22.5/30PR acceptance — 3/4 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs3
open_issues1
closed_issues2
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.667
closed_unmerged_prs1
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
16.9/25Owner reach — 221 followers of adidas
23.2/25Track record — 34 public repos, account ~8 yr old
Inputs used
followers221
owner_typeOrganization
is_verifiedyes
owner_loginadidas
public_repos34
account_age_days3,209

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 62 days ago
20/20Version history — 25 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageslakehouse-engine
ecosystemspypi
any_deprecatedno
min_days_since_publish62

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.isort])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://adidas.github.io/lakehouse-engine-docs/
10/10Repository description
10/10Topics — 10 topics
0/10Wiki
Inputs used
topicsbig-data, configuration-driven, data-engineering, data-quality, databricks, delta-lake, framework, great-expectations, lakehouse, spark
has_wikino
homepagehttps://adidas.github.io/lakehouse-engine-docs/
docs_sitehttps://adidas.github.io/lakehouse-engine-docs/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

17Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestscicd/requirements.txt, cicd/requirements_azure.txt, cicd/requirements_cicd.txt, cicd/requirements_dq.txt, cicd/requirements_os.txt, cicd/requirements_sftp.txt, cicd/requirements_sharepoint.txt, pyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.
How it's scored
14/35Direct dependencies free of known advisories — 1 affected: urllib3 2.7.0 (high 7.5)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories6
affected_packages1
assessed_packages13
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:lakehouse-engine@2.2.0 runtime dependency closure — what installing the published package pulls in — 13 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.

65Good · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
3.4/40Legible commit history — 2 of 31 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.065
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes9,077
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.isort])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 31
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
54.8/55Manageable file sizes — 1/266 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes75,672
source_files_sampled266
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — samples
Inputs used
example_dirssamples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

293GitHub stars
1contributors
4commits, last 12 months
62days since last push
21releases
1bus factor
1open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'lakehouse-engine' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for lakehouse-engine carried forward from the previous scan (stats endpoint unavailable this scan)
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 54 ⇿
0Stars
54Forks
18Releases

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.

01020304050605352023-102025-042026-09
Major 1Minor 12Patch 5

Each point covers 3 days.

All dependencies 77

Full resolved dependency set from the GitHub dependency graph: 0 direct and 77 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiohttp3.13.3indirect
PyPIaiosmtpd1.4.6indirect
PyPIazure-core1.38.0indirect
PyPIbandit1.8.6indirect
PyPIblack24.4.0indirect
PyPIboto31.40.23indirect
PyPIbuild1.3.0indirect
PyPIbump2version1.0.1indirect
PyPIcachecontrol0.14.4indirect
PyPIdelta-spark4.0.0indirect
PyPIdistlib0.3.6indirect
PyPIfilelock3.20.3indirect
PyPIflake87.3.0indirect
PyPIflake8-black0.3.6indirect
PyPIflake8-bugbear24.12.12indirect
PyPIflake8-builtins3.0.0indirect
PyPIflake8-cognitive-complexity0.1.0indirect
PyPIflake8-comprehensions3.16.0indirect
PyPIflake8-docstrings1.7.0indirect
PyPIflake8-eradicate1.5.0indirect
PyPIflake8-expression-complexity0.0.11indirect
PyPIflake8-isort6.1.2indirect
PyPIflake8-mutable1.2.0indirect
PyPIflake8-quotes3.4.0indirect
PyPIghp-import2.1.0indirect
PyPIgreat-expectations1.11.0indirect
PyPIgriffe1.15.0indirect
PyPIh24.3.0indirect
PyPIimportlib-resources6.5.2indirect
PyPIisort6.0.1indirect
PyPIjinja23.1.6indirect
PyPIlxml6.0.0indirect
PyPImarkdown3.10indirect
PyPImarkdown-callouts0.4.0indirect
PyPImarkdown-exec1.12.1indirect
PyPImarkdown-include0.8.1indirect
PyPImarshmallow3.26.2indirect
PyPImergedeep1.3.4indirect
PyPImike2.0.0indirect
PyPImkdocs1.6.1indirect
PyPImkdocs-autorefs1.4.3indirect
PyPImkdocs-gen-files0.6.0indirect
PyPImkdocs-literate-nav0.6.2indirect
PyPImkdocs-macros-plugin1.5.0indirect
PyPImkdocs-material9.7.1indirect
PyPImkdocs-material-extensions1.3.1indirect
PyPImkdocs-section-index0.3.10indirect
PyPImkdocstrings1.0.0indirect
PyPImkdocstrings-crystal0.3.9indirect
PyPImkdocstrings-python2.0.1indirect
PyPImoto4.2.14indirect
PyPImsal1.32.3indirect
PyPImsgraph-sdk1.40.0indirect
PyPImypy1.17.1indirect
PyPInest-asyncio1.6.0indirect
PyPIparamiko4.0.0indirect
PyPIpendulum3.1.0indirect
PyPIpip-audit2.10.0indirect
PyPIpip-tools7.5.0indirect
PyPIpymdown-extensions10.20indirect
PyPIpynacl1.6.2indirect
PyPIpyspark4.0.0indirect
PyPIpytest8.4.1indirect
PyPIpytest-cov6.2.1indirect
PyPIpytest-sftpserver1.3.0indirect
PyPIpyyaml6.0.2indirect
PyPIpyyaml-env-tag0.1indirect
PyPIregex2023.6.3indirect
PyPIrequests2.32.4indirect
PyPItenacity9.0.0indirect
PyPItwine5.1.1indirect
PyPItypes-boto31.40.23indirect
PyPItypes-paramiko2.12.0indirect
PyPItypes-requests—indirect
PyPIurllib32.6.3indirect
PyPIwatchdog3.0.0indirect
PyPIwerkzeug3.1.6indirect
Dependency advisories 1

Installing pypi:lakehouse-engine@2.2.0 pulls in 13 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
urllib32.7.0directhigh62.8.0

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

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

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.