Live, event-based analytics on Microsoft Fabric Eventhouses or Databricks structured streaming, for the small number of cases where yesterday's data genuinely isn't good enough.
Most businesses don't need real-time analytics. A well-built daily or hourly pipeline covers most reporting needs. This page is for the cases where it genuinely matters: operational monitoring, fraud detection, IoT telemetry, anything where a delay of hours actually costs something. If that's not your situation, our Data Warehouse & ETL/ELT page is the right starting point instead.
Real-time analytics means analysing data as it's generated, with minimal delay between an event happening and it showing up in a dashboard. It's not a default. It's a specific answer to a specific problem.
It earns its complexity in a narrow set of cases: fraud detection where minutes matter, equipment monitoring where a fault needs catching before it becomes a failure, operational dashboards tracking something genuinely live, not a monthly sales report that's perfectly fine refreshing overnight.
Fabric's Real-Time Intelligence workload is built around Eventhouses, databases purpose-built for streaming, time-based data, capable of querying billions of events in seconds. Data arrives through Eventstreams from sources like Kafka, SDKs, or other pipelines, and lands pre-organised for fast time-based search.
For organisations already on Fabric, this is the natural place to build a streaming layer. It sits alongside your existing Lakehouse or Warehouse rather than requiring a separate platform.
Databricks treats streaming as a first-class extension of the same Lakehouse architecture used for batch data. Apache Spark Structured Streaming and Delta Live Tables process unbounded data as it arrives, using the same medallion pattern and the same governance as everything else in the platform.
For organisations already running Databricks for engineering and modelling, streaming is an extension of infrastructure you already have, not a new system bolted on.
Production line monitoring · warehouse operations · system health and uptime monitoring · call centre queue management.
Suspicious transaction monitoring · unusual user activity detection · sales and operational anomaly alerts · website outage detection.
Temperature and environmental monitoring · equipment performance tracking · vehicle and asset tracking · energy consumption monitoring.
Real-time warehouse visibility · logistics and supply chain tracking · manufacturing floor monitoring · service desk and support operations.
That's worth figuring out honestly before building anything. A lot of "real-time" requests turn out to be solved just as well by a faster scheduled refresh, at a fraction of the complexity and cost.
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