Skip to main content

Copier Badge

nmdc-lakehouse-schema

The NMDC flattened-schema product. It owns the definition of the flat, tabular shape the NMDC lakehouse writes to Parquet, independent of the data-generation, publication, and validation code that consumes it:

  • transforms.schema_generator — generates the flat LinkML target schema from a source schema (e.g. nmdc-schema).
  • transforms.flatteners — the schema-driven flattener that turns supported nested LinkML shapes into flat rows. See the support guide for depth limits and known mismatches.
  • transforms.schema_diff — diffs two generated flat schemas so "what changed and why" is answerable.

Downstream ETL (the nmdc-lakehouse repo) is migrating to this package in PR #340. Until that change lands, its main branch still uses its local engine and saved schema.

Projection version 1.2.0 extracts TextValue raw strings into columns on the containing record. Single values become strings and repeated values become string arrays; TextValue-only child tables are removed. Collection record types remain available for polymorphic dispatch. See the TextValue projection contract for examples, validation behavior, and migration from earlier artifacts.

Documentation Website

https://microbiomedata.github.io/nmdc-lakehouse-schema

Development and documentation

From an updated feature checkout:

just install
just generate-flat-schema
just check-flat-schema
just test
just test-dist
just gen-doc
uv run mkdocs build

A merge to main rebuilds and deploys documentation from the checked-in schema. It does not regenerate that canonical artifact or publish a new Python package. Run just --list to see available recipes and just --dry-run gen-doc to inspect resolved documentation commands.

The source input is pinned to tagged nmdc-schema release 11.24.0. With projection 1.2.0, the current target is 11.24.0+flat.1.2.0 and has 59 table classes. See the upgrade guidance for renamed fields, release verification, and production preflight requirements.

Repository structure

Location Purpose
src/nmdc_lakehouse_schema/transforms/ Generator, runtime flattener, and schema-diff implementation.
src/nmdc_lakehouse_schema/schema/nmdc_schema_flattened.yaml Canonical generated product; regenerate it instead of editing by hand.
src/nmdc_lakehouse_schema/schema/nmdc_lakehouse_schema.yaml Remaining Person/PersonCollection template example; removal tracked in #16.
src/nmdc_lakehouse_schema/datamodel/ Generated models for that template example, not the NMDC flat product.
src/docs/ Authored documentation; edit these sources.
docs/, site/ Ignored assembled Markdown and built HTML.
tests/ Projection/artifact regression tests and remaining template fixtures.
project/, examples/output/ Ignored output from template-generation/example recipes.

Credits

This project uses the template linkml-project-copier.

Metadata

Release files for nmdc-lakehouse-schema 0.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nmdc-lakehouse-schema 0.4.0
File Size Uploaded Provenance Actions
nmdc_lakehouse_schema-0.4.0.tar.gz 387.4 kB With provenance Details for nmdc_lakehouse_schema-0.4.0.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for nmdc-lakehouse-schema 0.4.0
File Interpreter ABI Platform Actions
nmdc_lakehouse_schema-0.4.0-py3-none-any.whl Python 3 none any Details for nmdc_lakehouse_schema-0.4.0-py3-none-any.whl

Total release size: 516.4 kB

Release files / nmdc_lakehouse_schema-0.4.0.tar.gz

Download URL nmdc_lakehouse_schema-0.4.0.tar.gz
Size 387.4 kB
Tags Source
SHA-256 checksum
How to use checksums
618ea4edf00e1dfc9fa587970ef94880040e9dba563a714a4a116312b275a472
BLAKE2b-256 checksum
How to use checksums
8e19f86280e20fb2d42356cafa41e33795bad883554500ea4e273b9721ca3ee0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 22, 2026.

Transparency log

Release files / nmdc_lakehouse_schema-0.4.0-py3-none-any.whl

Download URL nmdc_lakehouse_schema-0.4.0-py3-none-any.whl
Size 129.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
e88f486fe4245290e1b0ea8825d134c99dbd09b44db5952cc602f2d99e292169
BLAKE2b-256 checksum
How to use checksums
cb4ec193806b1349f655a2d52497eccfc49739d36b2275c9e0795ff29af6a43f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 22, 2026.

Transparency log

Security for nmdc-lakehouse-schema 0.4.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

Both files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for nmdc_lakehouse_schema-0.4.0.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 22, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page