Oct 12, 2023 · The Azure Data Explorer web UI has a samples gallery for you to practice writing Kusto Query Language (KQL) queries and commands. This gallery provides sample data with guided tutorials in a free and publicly accessible help cluster .

A Kusto query is a read-only request to process data and return results. The request is stated in plain text, using a data-flow model that is easy to read, author, and automate. Kusto queries are made of one or more query statements.

Dec 4, 2024 · Kusto Query Language (KQL) is used to write queries in Azure Data Explorer, Azure Monitor Log Analytics, Azure Sentinel, and more. This tutorial is an introduction to the essential KQL operators used to access and analyze your data.

Dec 25, 2024 · Kusto Query Language (KQL) is a powerful query language used primarily for querying Azure Data Explorer, Log Analytics, and Application Insights. Its intuitive syntax and robust capabilities make...

Dec 10, 2019 · Kusto Query Language is a powerful intuitive query language, which is being used by many Microsoft Services. KQL Language concepts . Relational operators (filters, union, joins, aggregations, …) Can be combined with ‘|’ (pipe). Similarities: OS shell, Linq, functional SQL…

Feb 1, 2021 · Here are a few examples: Refer to d, h, m, and s for days, hours, minutes and seconds. Multiply numeric values (such as double and long) by a timespan value to get a timespan value. Returns up to the specified number of rows. Returns the first N records sorted by the specified columns.

Feb 8, 2017 · Example queries for learning the Kusto Query language in Azure Data Explorer. Kusto can be used in Azure Monitor Logs, Application Insights, Time Series Insights and Defender Advanced Threat Perception.

Jun 12, 2022 · This post will explore some Kusto query language (KQL) syntax through examples. If you are not familiar with KQL you can read Kusto Query Language (KQL) overview from Microsoft's documentation website.

Mar 23, 2023 · Kusto Query Language (KQL) is a powerful query language to analyse large volumes of structured, semi structured and unstructured (Free Text) data. It has inbuilt operators and functions that lets you analyse data to find trends, patterns, anomalies, create forecasting, and machine learning.

After creating tables and ingesting data to them we can move forward and use Kusto Query Language (aka KQL) to explore the data. We can use such queries to discover patterns, identify anomalies and outliers, create statistical modeling and more.