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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

Loom Eventstream Editor — Fabric-parity spec

Comparative positioning note

This document is written from the perspective of Microsoft Azure, Cloud Scale Analytics, and CSA Loom. Any description of third-party or competing products, services, pricing, or capabilities is derived from publicly available documentation and sources believed accurate at the time of writing, and is provided for general comparison only. We do not claim expertise in, or authority over, any non-Microsoft product or service; the respective vendor's official documentation is the authoritative source for their offerings, which may change over time. Nothing here is intended to disparage any vendor — where a competing product has genuine advantages, we aim to note them honestly. Verify all third-party details against the vendor's current official documentation before making decisions.

Captured 2026-05-26 by catalog agent a8260c3697beb6c69 from Fabric documentation + casino-fabric-poc workspace inspection. No existing Eventstream in workspace, so spec is from Fabric docs + sample exploration.

Fabric UX

Visual designer canvas

Three-section layout: ribbon/canvas + configuration pane (right) + test result pane (bottom). Drag-and-drop nodes connect source → transform → destination. Real-time preview + authoring error validation.

Source picker — types

External streaming: Azure Event Hubs · Kafka (Confluent / Apache / Amazon MSK) · Azure IoT Hub · Azure Event Grid · Azure Service Bus · Google Cloud Pub/Sub · Amazon Kinesis Data Streams · MQTT · Solace PubSub+ · Cribl

Change Data Capture (CDC): Azure SQL Database · PostgreSQL · MySQL · Azure Cosmos DB · SQL Server on VM · Azure SQL Managed Instance · MongoDB

Fabric: Workspace item events · OneLake events · Job events · Capacity overview events

Data sources: Azure Blob Storage events · Azure Data Explorer · Real-time weather · Sample data (Bicycles / Yellow Taxi / Stock Market / Buses / S&P 500 / Semantic Model Logs)

Custom: Custom endpoint (HTTP / custom app with connection string)

Transform nodes (event processor operations)

Filter · Aggregate (sum/min/max/avg over time windows) · Group by (window-based aggregations) · Manage fields (add/remove/rename/change type) · Join · Union · Expand (array → rows) · SQL operator (preview)

Destination picker

Lakehouse (Delta) · Eventhouse (direct or processed ingestion modes) · Activator (intelligent alerting) · Custom endpoint · Derived stream (intermediate) · Spark Notebook (preview, Spark Structured Streaming)

Additional features

  • Apache Kafka endpoint for native client integration
  • Schema management with Fabric Schema Registry
  • DeltaFlow (analytics-ready CDC streams)
  • Pause/resume controls for derived streams
  • Workspace Private Link support

What Loom needs

Component Status Loom build target
Visual canvas ❌ none New React component with react-flow or similar
Source picker partial (Cosmos persistence) Wire to Event Hubs / Cosmos CDC / ADX / Blob Storage event registrations
Transform nodes partial (Cosmos persistence) Implement Filter/Aggregate/GroupBy/Join/Union via Azure Stream Analytics OR Kusto inline ingestion mappings
Destination Lakehouse ❌ Wire to existing Loom Lakehouse (writes Delta to ADLS)
Destination Eventhouse partial Wire to existing Loom ADX (Eventhouse equivalent)
Destination Activator ❌ Wire to existing loom-activator-engine container app

Backend mapping

  • Event Hubs for streaming ingress (need to deploy in bicep — Microsoft.EventHub/namespaces)
  • Azure Stream Analytics OR ADX inline ingestion mappings for transforms
  • ADX .ingest commands when destination is Eventhouse
  • Loom Activator engine container (already deployed) for Activator destination

Required Azure resources (currently missing)

  • Event Hubs namespace + hub
  • Azure Stream Analytics job (optional, if not using ADX-only path)
  • Kusto ingestion mapping resources

Estimated effort

3-4 sessions. Stream designer alone is 1-2 sessions of UI work. Backend wiring + bicep is another. Then per-source + per-destination wiring is incremental.