Data contract (ODCS) in MetaCroc #

MetaCroc supports Open Data Contract Standard (ODCS)–oriented metadata so you can describe a data product contract in the catalog before exporting it to downstream tools such as a data catalog or runtime data-quality engines.

Contract information is split into two layers:

Header metadata on a Folder — status, tenant, purpose, usage, limitations
Data quality rules on individual Table columns — Great Expectations checks stored as ODCS DQ rules

When to use it #

Use ODCS metadata when you are modelling a data product (a folder subtree with related tables) and want to:

Document contract status and business context for stewards and consumers
Capture column-level quality expectations alongside physical table design
Prepare metadata for future ODCS export or integration with Great Expectations–based pipelines

How the pieces connect #

Folder (data product root)
├── Data contract (ODCS) header     ← status, tenant, purpose, usage, limitations
└── Tables
    └── Columns
        └── Great Expectations checks   ← ODCS DQ rules per business column

The Business domain field on folder or table Overview (see Overview) complements ODCS metadata and maps to the contract domain concept in export scenarios.

Typical workflow #

Open the data-product folder and go to Overview.
Fill in Data contract (ODCS) fields — at minimum set Contract status and document Purpose / Usage as needed. Set Tenant on the root product folder if subfolders should inherit it.
Define or update Tables and Columns as usual.
On each relevant business column, add Great Expectations checks from the Columns tab.
Save / Push the folder and tables so ODCS metadata and column rules are persisted.

What is available today (Release 200) #

Capability	Location	Documentation
Contract header (status, tenant, markdown fields)	Folder → Overview	Data contract on Folder
Great Expectations column checks	Table → Columns	Column data quality checks

Planned extensions #

MetaCroc’s underlying data model supports a broader set of data-quality rule types for future releases:

ODCS library metrics (for example null values, duplicate values)
SQL rules with operators
Free text descriptions without machine execution
Databricks DQX checks