--- title: Automagic Data Anomaly Detection in Fabric | Data... description: A demo walkthrough showing how to build an automatic data anomaly detection pipeline using Azure SQL change-about streaming, Microsoft Fabric event streams,... canonical: https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition | daily.dev og:description: A demo walkthrough showing how to build an automatic data anomaly detection pipeline using Azure SQL change-about streaming, Microsoft Fabric event streams,... og:url: https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f og:image: https://api.daily.dev/og/posts/o12ZyIy9f.png og:image:alt: Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition **[Microsoft Developer](https://daily.dev/sources/microsoftdeveloper)** · 11 min read · 0 upvotes · 0 comments ## Summary A demo walkthrough showing how to build an automatic data anomaly detection pipeline using Azure SQL change-about streaming, Microsoft Fabric event streams, and KQL's series_decompose_anomalies function. The scenario simulates bot-driven ticket fraud for a fictional soccer tournament, streaming transactional data from Azure SQL Hyperscale into a Fabric EventHouse (KQL/Kusto database), then using built-in statistical anomaly detection to flag spikes without manually defining thresholds. The Fabric Activator can then trigger automated responses like sending alerts or running notebooks/pipelines when anomalies are detected. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I detect anomalies in streaming data using KQL in Microsoft Fabric? Use the series_decompose_anomalies function in a KQL query against an Eventhouse database, specifying a time window such as the last 15 minutes; it automatically flags anomalous rows with a score representing roughly how many standard deviations they are from normal, without requiring you to define what counts as an anomaly manually. _daily.dev surfaces practical walkthroughs like this for engineers wiring up anomaly detection pipelines._ ### What is change streaming in Azure SQL and how is it different from change data capture (CDC)? Change streaming captures changes from the transactional log and pushes them directly to an external destination such as a Fabric event stream or Azure Event Hub, unlike change tracking or change data capture which record changes for later querying inside the database. It requires a database credential with a managed identity authorized to connect to Fabric. _Developers evaluating Azure SQL streaming options can track these patterns via daily.dev._ ### How can I trigger an automated action when Fabric detects a data anomaly? Create an Activator alert on the anomaly output, choosing an action such as sending a message to a team, running a custom action, triggering another event, running a notebook, or running a pipeline. The alert reacts directly to the anomaly score produced by the KQL query in the Eventhouse. _daily.dev helps engineers building automated data-quality responses keep up with tools like Fabric Activator._ ## Similar posts on daily.dev - [Building Event-Driven Data Agents with BigQuery, Pub/Sub, and ADK](https://daily.dev/posts/building-event-driven-data-agents-with-bigquery-pub-sub-and-adk-rudxhveuw) · Google Cloud · 0 upvotes · 0 comments - [5 Best Practices for Automated Anomaly Detection in Data Pipelines](https://daily.dev/posts/5-best-practices-for-automated-anomaly-detection-in-data-pipelines-2dx0liztg) · Decube · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#azure](https://daily.dev/tags/azure), [#change-data-capture](https://daily.dev/tags/change-data-capture), [#microsoft-fabric](https://daily.dev/tags/microsoft-fabric) [View this post on daily.dev](https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition","url":"https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f"},"datePublished":"2026-07-28T16:20:04.326Z","dateModified":"2026-09-14T06:51:32.423Z","description":"A demo walkthrough showing how to build an automatic data anomaly detection pipeline using Azure SQL change-about streaming, Microsoft Fabric event streams,...","image":"https://i.ytimg.com/vi/2j-y_YtR2r0/sddefault.jpg","thumbnailUrl":"https://i.ytimg.com/vi/2j-y_YtR2r0/sddefault.jpg","isAccessibleForFree":true,"articleSection":"Microsoft Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Microsoft Developer","logo":"https://media.daily.dev/image/upload/s--xZzU3zUq--/f_auto/v1746714872/logos/microsoftdeveloper","url":"https://daily.dev/sources/microsoftdeveloper"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,azure,change-data-capture,microsoft-fabric","timeRequired":"PT11M","video":{"@type":"VideoObject","name":"Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition","description":"A demo walkthrough showing how to build an automatic data anomaly detection pipeline using Azure SQL change-about streaming, Microsoft Fabric event streams,...","thumbnailUrl":"https://i.ytimg.com/vi/2j-y_YtR2r0/sddefault.jpg","uploadDate":"2026-07-28T16:20:04.326Z","duration":"PT11M","url":"https://api.daily.dev/r/o12ZyIy9f","embedUrl":"https://www.youtube.com/embed/2j-y_YtR2r0"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Microsoft Developer","item":"https://daily.dev/sources/microsoftdeveloper"},{"@type":"ListItem","position":3,"name":"Automagic Data Anomaly Detection in Fabric | Data Exposed MVP Edition"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/automagic-data-anomaly-detection-in-fabric-data-exposed-mvp-edition-o12zyiy9f#faq","mainEntity":[{"@type":"Question","name":"How do I detect anomalies in streaming data using KQL in Microsoft Fabric?","acceptedAnswer":{"@type":"Answer","text":"Use the series_decompose_anomalies function in a KQL query against an Eventhouse database, specifying a time window such as the last 15 minutes; 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