Domain catalogue Reference data on Databricks

Units of measure on Databricks

Units and their conversions as one governed list, with the item-specific conversions that purchasing, sales and planning rely on.

SOURCES, READ IN PLACEDatabrickslakehouseASSEMBLED1 · standardise2 · match on the domain keys3 · survive field by field4 · place in the hierarchies5 · run the column and row rules6 · publish the golden viewGOLDEN UNITS OF MEASUREUnits of measureone id · every source key keptREAD BYGrids, pivots and drill-downswith the transactionsAPIs, SQL and agentsone policyWrite-back to the systemsacknowledged
The objects the domain reads in place on Databricks, the assembly steps, and who reads the result. Writes go through MERGE INTO a governed catalog.
2
objects read in place from Databricks
catalogue
4
attribute groups assembled
catalogue
1 days
to production-grade
estimate
1
write path, acknowledged and logged
catalogue
0
rows copied to start
product
What the domain reads

The Databricks objects behind the golden record

Delta tables are read in place through Unity Catalog with filters and aggregates pushed down; nothing is copied out.

ObjectRead fromFields the domain usesFreshness
Unitsilver.uomcode · iso · dimension · nameas fresh as the landing pipeline; live for governed tables
Conversionsilver.uom_conversionmaterial_key · from_uom · to_uom · numerator · denominatoras fresh as the landing pipeline; live for governed tables
The keys

How units of measure records match, in this system's fields

ISO codecode per system in the crosswalk

The first object above carries the strong keys; the standardised attributes come from the same rows. Item-specific conversions are validated against the global ones, so a case always weighs at least its contents.

What is written back

Through the interfaces Databricks supports

Golden records and their history are written with MERGE INTO into a governed catalog, registered in Unity Catalog with lineage.

InterfaceUsed forResult
MERGE INTO gold.uom on the golden key; history in gold.uom_history; registered in Unity Cataloggolden values approved for write-back: unit; global conversions; item conversionsthe id or error Databricks returns, stored beside the change

Write-back is off per object until a test write succeeds, and approval rules can require a person per object. Reconciliation compares the source row with the golden value on a schedule.

Access and prerequisites

What is in place before the first build

Unity Catalog grants and row filters are honoured; Lima adds its own layers on top.

Register the objects2 objects from the connector catalogue; each shows its rows at once.
Nearby

This domain on other systems, and other domains on Databricks

Units of measure on

Other domains on Databricks

Terms on this page

DimensionWhat a unit measures: count, mass, length, volume, time.
Base unitThe unit an item's stock is counted in.
Read in placeThe rows stay in Databricks; Lima sends the filter and the columns and Databricks returns the matching rows.
Questions

Does this domain copy data out of Databricks?

The golden record reads the objects above in place. History the pivot needs at scale is landed in your lake by the Backbone, under the same keys, security and audit.

What does the Databricks team need to do?

Grant the read access and the write interfaces listed above and agree the refresh method. Nothing is installed in Databricks.

Can we start with reads only?

Yes. Write-back stays off per object until a test write succeeds; the golden view, the API and the pivots work from day one on reads.

See your first domain built on your data

Bring two systems that hold the same customers, suppliers or materials. In one working day we connect them, run the match on live rows, review the queue with your steward and publish the first golden view with its API.