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CSA Loom — the Microsoft Fabric experience for Azure tenants where Fabric isn't yet available: lakehouses, warehouses, notebooks, semantic models, Activator rules, Data Agents, across Commercial, GCC, GCC-High, and DoD IL5

Workshop datasets — CUI-safe synthetic data

All CSA Loom workshop labs run on synthetic, CUI-safe data. Every value is machine-generated; no real PII, PHI, CUI, or production records are present. The datasets are safe to use in any boundary (Commercial, GCC, GCC-High) and in recorded sessions.

CUI-safe by construction

These datasets contain only fabricated identifiers and values. They are designed for instruction, not analysis of real populations. Do not augment them with real records during a workshop — keep the boundary clean.

The three datasets

Dataset Used in Vertical fit
Synthetic IoT Days 2-4 (primary) Manufacturing, utilities, transportation, defense telemetry
Synthetic financial transactions Day 3 alternate FSI, regional banks, fraud analytics
Synthetic clinical encounters Day 3 alternate (Commercial) Healthcare (HIPAA), pharma

How datasets load into Loom

Each dataset page documents the schema, a sample of rows, and the load path. Labs load data through the real Loom panes and provisioners — there is no mock data:

  • Lakehouse → Get data (/lakehouse) uploads the CSV into a Bronze Delta table via the lakehouse provisioner (ADLS Gen2 + Delta).
  • KQL ingest (.ingest inline) loads event rows into the Azure Data Explorer (ADX) cluster for the real-time labs, matching the seed pattern used by the kql-db provisioner.
  • Mirrored database labs use a pre-provisioned synthetic operational source whose connection string the facilitator supplies at the workshop.

Regenerating / extending

The datasets are intentionally small (workshop-sized). To regenerate or grow them for a longer engagement, the schema definitions on each page are the contract — generate more rows that honor the schema and the CUI-safe rule (no real values). Keep row counts modest so labs stay within the 8-hour day.