Manufacturing and quality on Databricks

Batch release on Databricks

Takes a produced batch from complete inspection results through QA review to a usage decision, sets the batch status in the ERP and produces the certificate of analysis.

SCREENSDATA HANDLERSDATABRICKS · READ IN PLACES01 Batches awaiting releaseS02 Batch record reviewS03 DeviationsS04 Usage decisionS05 CertificateS06 Boarddh_batchBatchsilver.batchdh_inspection_lotInspection lotsilver.inspection_lotdh_production_orderProduction ordersilver.production_orderdh_quality_notificationQuality notificationsilver.quality_notificationdh_stock_positionStock positionsilver.stock_position · gold.stock_dailyWRITE BACK TO DATABRICKSInspection lot → MERGE INTO gold.lima_app_decisionsBatch → MERGE INTO gold.lima_app_decisionsrows stay in Databricks · access rules applied at read time · each write acknowledged and logged
The screens of the app, the data handler behind each, and the Databricks object it reads in place. Writes below the line go through MERGE into a Lima-owned table, with the operational write to the ERP of record.
5
objects read in place from Databricks
catalogue
6
screens on those rows
catalogue
6 days
to production-grade
estimate
2
objects written back
catalogue
0
rows copied to build it
product
What the app reads

The Databricks objects behind each handler

Delta tables in Unity Catalog are read in place through a SQL warehouse: the app sends only the filter and the columns it needs. The Backbone landing model (bronze, silver and gold) is the usual source, so history and aggregates are already prepared and lineage is registered in Unity Catalog.

ObjectRead fromFields the app usesFreshness
Batchsilver.batchbatch_id (batch) · material_id (material) · expiry_date (expiry) · production_date (produced)prepared by the Backbone; refreshed on its schedule
Inspection lotsilver.inspection_lotresult (result) · usage_decision (decision) · origin (origin) · lot_id (lot)prepared by the Backbone; refreshed on its schedule
Production ordersilver.production_orderorder_id (number) · material_id (material) · order_qty (qty) · start_date (start)prepared by the Backbone; refreshed on its schedule
Quality notificationsilver.quality_notificationnotification_id (number) · type (type) · material_id (material) · vendor_id (vendor)prepared by the Backbone; refreshed on its schedule
Stock positionsilver.stock_position · gold.stock_dailymaterial_id (material) · plant (plant) · storage_location (sloc) · on_hand_qty (on hand)prepared by the Backbone; refreshed on its schedule
The trigger

The start criterion in Databricks's own fields

In plain words: An inspection lot from production (origin 04) whose results are recorded for every characteristic and whose usage decision is still open.

The criterion runs in Databricks through the data handler, so only the matching rows return. The editor shows the rows it selects, and their count, as it is written.

# start criterion in Databricks fields
origin = '04' and result = 'recorded' and usage_decision = ''

# the same criterion in canonical fields
{lot.origin} = '04' and {lot.result} = 'recorded' and {lot.decision} = ''
What the app writes back

Through MERGE and the system of record

Decisions are recorded in a Lima-owned gold table with MERGE INTO, so notebooks and dashboards see them next to the history. The operational write goes to the system of record through its released interface, and the app records the acknowledgement. For this app the operational change (usage decision posted as accept or reject through the inspection lot interface, with the stock posting from quality inspection to unrestricted, blocked or scrap) goes to SAP S/4HANA or Oracle Fusion through its released interface, and the pair pages for those systems show the exact call.

ObjectWhat changesInterface
Inspection lotusage decision posted as accept or reject through the inspection lot interface, with the stock posting from quality inspection to unrestricted, blocked or scrapMERGE INTO gold.lima_app_decisions
Batchbatch status set to released or blocked; certificate of analysis attached to the batchMERGE INTO gold.lima_app_decisions
Acknowledged and logged

Each write stores the identifier Databricks returns beside the decision, the person and the time, so the log answers who changed what, and the reconciliation shows it arrived.

Access and prerequisites

What is in place before the first build

Unity Catalog grants and row filters are mapped to Lima access policies once, so a person sees the same rows in the app that they see in a notebook.

  • A service principal with SELECT on the silver and gold schemas and MODIFY on the Lima decision schema
  • A SQL warehouse (a small serverless warehouse serves most screens)
  • The Backbone landing model in place for the objects the app uses, or your own silver tables registered
  • The system of record connected for the operational write
Register the handlers5 objects from the catalog; each shows its rows at once.
Why the lake

History and aggregates, read where they land

This app reads batch from Databricks because the screens need history at scale: months of rows, aggregates by period, several versions. The Backbone lands the objects in the conformed model and refreshes them on a schedule, and the handler reads them in place with the filter and the columns pushed down. The person's grants in Databricks decide what they see.

The operational rows the person acts on can come live from the ERP in the same app: one handler per object per system, and a screen that shows both.

Readsconformed and history tables, in placeDatabricks
Decision recordMERGE into a Lima-owned tablebeside the history
Operational writeto the ERP of record through its interfaceacknowledged
GrantsDatabricks roles and row policies honouredat read time
Nearby

This app on other systems, and other apps on Databricks

Batch release on

Other apps on Databricks

Terms on this page

Usage decisionThe decision recorded on an inspection lot that accepts or rejects the inspected quantity and posts its stock accordingly.
Quality inspection stockStock that waits for a usage decision; it becomes available for delivery once posted to unrestricted.
Read in placeThe rows stay in Databricks; the handler sends the filter and the columns and Databricks returns the matching rows.
Questions

Does this app copy data out of Databricks?

The screens read through handlers that leave the rows in Databricks. The conformed tables it reads were landed there by the Backbone from the ERP; the app reads them there.

What does Databricks need to allow?

The prerequisites above: an integration identity with read rights on the objects listed and write rights on the interfaces named, connectivity, and the scopes agreed with the process owner.

Can I run it on another system as well?

Yes. The screens, criteria and roles stay; the handlers are re-bound to the other system's objects, and the app's other system pages show them.

See this app on your data

Bring the process and the systems it touches. In the first session we register the data handlers, draw the levels or the lanes and run the first screen on your live rows.