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M3 Reference

MNS100

Interactive

Company — connect division: where the CMNDIV divisions under a company are created and maintained

Tables vs APIs

This program works over the tables below. For analytics at scale, land the raw tables — the SQL further down reads them directly, joined on their keys and CONO. When to land tables vs call MI APIs

Boilerplate SQL

Databricks SQL

Starting point for reading the tables behind MNS100 from landed data — the backing tables joined on their keys and CONO. Set your Unity Catalog location, company, and filter values below.

Query parameters

3 parameters not filled: <catalog>, <schema>, <company>

-- ============================================================
-- Program: MNS100 — Company — connect division: where the CMNDIV divisions under a company are created and maintained
-- Purpose: Read the tables behind program MNS100 — auto-generated from program-table-map
-- Grain  : One row per CMNDIV record
-- Tables : CMNDIV
-- Notes  : Auto-generated skeleton for a prefixed physical M3 schema or a landing schema normalized to the prefixed names in this catalog. Raw Data Lake property names vary with the published object: map them through Data Catalog before running this SQL. Dates are numeric YYYYMMDD (0 = none, mapped to NULL); all curated status values are decoded inline; company-partitioned tables are joined on CONO to prevent cross-company fan-out. Audit columns (RGDT/RGTM/LMDT/CHNO/CHID) omitted — see the quirks guide.
-- ============================================================
SELECT
  h.CCCONO AS "Company",
  h.CCDIVI AS "Division — the code divisional and financial tables carry",
  h.CCTX40 AS "Division name"
FROM <catalog>.<schema>.CMNDIV h
WHERE
  h.CCCONO = <company>
ORDER BY h.CCCONO;

Backing Tables

Maintained by Summit Analytics, a supply chain analytics practice. The tools and references are free — the consulting is selective.

Part of the Summit Analytics reference library.

Work with the practice

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