Routes every quote whose discount passes the band to the sales manager and then pricing, and returns the approved status and discount to the quote.
Tables are read in place through the native Snowflake connector: the app sends only the filter and the columns it needs, Snowflake returns the matching rows. The Backbone landing model (RAW, CONFORMED and HISTORY schemas) is the usual source, so history and aggregates are already prepared.
| Object | Read from | Fields the app uses | Freshness |
|---|---|---|---|
| Quotation | CONFORMED.QUOTATION | net_value (value) · discount_pct (discount) · quote_id (number) · customer_id (customer) | prepared by the Backbone; refreshed on its schedule |
| Customer master | CONFORMED.CUSTOMER | customer_id (id) · name (name) · country (country) · sales_org (sales org) | prepared by the Backbone; refreshed on its schedule |
| Price condition | CONFORMED.PRICE_CONDITION | material_id (material) · customer_id (customer) · amount (amount) · currency (unit) | prepared by the Backbone; refreshed on its schedule |
| Sales order line | CONFORMED.SALES_ORDER_LINE | so_number (number) · so_line (line) · customer_id (customer) · material_id (material) | prepared by the Backbone; refreshed on its schedule |
In plain words: A quote saved with a line discount above 15 percent, or a quote value above 250,000.
The criterion runs in Snowflake 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 Snowflake fields discount_pct > 15 or net_value > 250000 # the same criterion in canonical fields {quote.discount} > 15 or {quote.value} > 250000
Decisions are recorded in a Lima-owned table in Snowflake through stage, COPY INTO and MERGE, so analytics 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 (approval status, approved discount per line and approver note written to the quote) goes to SAP S/4HANA or Oracle Fusion through its released interface, and the pair pages for those systems show the exact call.
| Object | What changes | Interface |
|---|---|---|
| Quotation | approval status, approved discount per line and approver note written to the quote; a returned quote goes back to draft with the note | MERGE into LIMA.APP_DECISIONS |
Each write stores the identifier Snowflake returns beside the decision, the person and the time, so the log answers who changed what, and the reconciliation shows it arrived.
Snowflake roles and row-access policies are mapped to Lima access policies once, so a person sees the same rows in the app that they see in a worksheet.
This app reads quotation from Snowflake 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 Snowflake 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.
The screens read through handlers that leave the rows in Snowflake. The conformed tables it reads were landed there by the Backbone from the ERP; the app reads them there.
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.
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.
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.
