Real-Time Intelligence with Microsoft Fabric

Chapter 9. Kusto Query Language

As organizations generate and collect vast amounts of data from multiple sources, they need to have a fast, scalable, and efficient query language. Kusto Query Language (KQL) is the response to that challenge, and it’s designed with a powerful, read-optimized nature that lets users quickly explore, visualize, and analyze large volumes of structured and semi-structured data.

KQL powers real-time analytics in Microsoft Fabric, as well as Azure Data Explorer (ADX), Microsoft Sentinel, and Azure Monitor. It is thus an essential tool for real-time telemetry, log analytics, and operational insights. Unlike traditional SQL, which is transactional and write optimized, KQL is designed for fast data retrieval and analysis, to enable pattern detection, anomaly identification, and inferential insight derivation using minimal compute resources.

This chapter will give you thorough overall information on KQL, beginning with its basic syntax and guiding you through all the advanced querying techniques like aggregations, joins, time-series analysis, and integration of machine learning. We’ll also examine how KQL differs from SQL, its strengths in handling large-scale streaming and batch data, and how to use it with Power BI for real-time data visualization purposes.

At the end of this chapter, you will know how to write and optimize KQL queries, and that will put you in a great position to tap into the actionable insights obtainable from real-time data sources in ...