Real-Time Dashboards in Git: Reviewable Changes and a Way Back
Put Fabric Real-Time Dashboards under Git so tile and query changes are reviewed, validated before Update from Git, and reverted cleanly when they break.
30 articles
Put Fabric Real-Time Dashboards under Git so tile and query changes are reviewed, validated before Update from Git, and reverted cleanly when they break.
How to catch a slipping Fabric pipeline before its deadline: alert on missing and late runs, not just failures, using job events, KQL and the jobs API.
Set Fabric Activator and KQL alert thresholds from the cost of a miss versus the cost of acting, then tier actions by the value at risk, not the raw reading.
Use update policies, materialized views and a KQL state-change function in a Fabric Eventhouse to strip duplicates, chatter and flapping before anyone queries.
How to split a slow Fabric pipeline run into start delay, queueing, Spark start-up, copy transfer and idle gaps, with KQL and the activity runs API.
Use KQL baselines, week-over-week comparison and diffpatterns in an Eventhouse to tell a meaningful operational change from ordinary noise, and to explain it.
How to design Fabric Activator and KQL queryset alerts that fire on transitions, carry an owner and a next step, and stop teams muting the channel.
Rules for Fabric Real-Time Dashboards that stay trusted: tiles with owners, visible data freshness, refresh costs you can afford, and thresholds in tables.
One sensor-monitoring design on Fabric Real-Time Intelligence: where to filter, enrich, aggregate and alert, and what Eventhouse retention costs you.
Why KQL is a baseline Azure skill, how its pipe model differs from SQL, and the operators, join defaults and has-vs-contains traps that matter most.
Eventhouse is the storage layer optimized for time-series and streaming data. It automatically indexes data for lightning-fast queries.
Microsoft Fabric's Real-Time Intelligence workload has matured significantly since GA. Today I'm exploring how to build streaming analytics pipelines that…
Microsoft Fabric's Real-Time Intelligence workload has matured significantly since GA. We've implemented several production streaming scenarios, and I want…
A decision guide for placing filters, enrichment, aggregation and alerts across Eventstream, Eventhouse and Spark in Fabric Real-Time Intelligence.
KQL Querysets are the analysis layer of Real-Time Intelligence in Fabric. Today I'm exploring how to write effective KQL for real-time analytics.
Eventhouses are the foundation of Real-Time Intelligence in Fabric. Today I'm exploring their architecture and capabilities in depth.
The KQL habits that keep queries over streaming telemetry fast in Fabric KQL databases and Azure Data Explorer, and the shortcuts that quietly slow them down.
Practical Fabric Eventstream patterns: what belongs in the no-code event processor, what belongs in KQL, and what to fix before events arrive.
How Eventstream, KQL databases, Power BI and the Data Activator preview fit together in Fabric as of January 2024, and when to choose something else.
'{ "SoftDeletePeriod": "90.00:00:00", "Recoverability": "Enabled" }' '{ "DataHotSpan": "7.00:00:00", "IndexHotSpan": "14.00:00:00" }'
'{' ' "SoftDeletePeriod": "30.00:00:00",' ' "Recoverability": "Enabled"' '}'
Real-Time Analytics in Fabric provides powerful streaming capabilities for time-sensitive workloads. Tomorrow, I will cover Power BI integration in Fabric.
KQL functions in Azure Data Explorer are the reusability mechanism that prevents the same complex query logic from being copy-pasted across dashboards…
Cross-workspace queries in KQL let you write a single query that spans multiple Log Analytics workspaces—essential when your monitoring architecture splits…
Azure Resource Graph is the query service that answers "what do we actually have in Azure?" at enterprise scale in milliseconds. The ARM APIs query each…
KQL is the query language I've spent the most hours in this year that most developers have never heard of. If you use Log Analytics, Sentinel, Application…
Design an Azure Monitor workbook for incident triage on workspace-based Application Insights, with sampling-safe KQL, drill-down and ARM deployment.
Designing an Azure Data Explorer database for device telemetry: queued vs streaming ingestion, hot cache sizing, materialized views and KQL time series.
Resource Graph is essential for understanding your Azure estate at scale.
Log Analytics and Azure Data Explorer share the Kusto engine; here is when a dedicated ADX cluster is worth it for log data and when it isn't.