Kusto Query Language (KQL) is a powerful query language used to interact with the Microsoft Azure Data Explorer (ADX) service. KQL is optimized for searching and analyzing large volumes of data, making it an ideal tool for data analysis, monitoring, and troubleshooting.
The language was later integrated with Log Analytics, another Microsoft cloud-based service (built on top of ADX) that allows users to collect and analyze log data from various sources.
KQL is a versatile language that can be used for a wide range of data analysis and management tasks. It can be used to query and analyze structured data, such as tables and databases, as well as unstructured data, such as log files and other text-based data sources.
Where did “Kusto” come from?
I read in multiple places that the internal code name for Azure Data Explorer, Kusto, was actually a nod to Jacques Cousteau (Cousteau = Kusto) both being explorers of oceans (one of literal oceans, the other of oceans of data).
I struggled to find a solid Microsoft reference but if you check out this example query on learn.microsoft.com, the dates referenced are milestones in Jacques Cousteau’s life, more or less confirming the relationship.
Key features of KQL
KQL offers a wide range of features that make it a popular choice among developers, data analysts, operations analysts, and security analysts, including:
- Simple Syntax: KQL has a simple and intuitive syntax that allows users to quickly write complex queries.
- Fast Performance: KQL is designed for fast and efficient querying of large volumes of data.
- Flexible Aggregation: KQL provides flexible aggregation options that allow users to summarize and analyze data in a variety of ways.
- Rich Data Types: KQL supports a wide range of data types, including strings, numbers, dates, times, and more.
- Extensible: KQL can be extended with user-defined functions, custom operators, and plugins.
KQL use case examples
Here are some examples of how KQL can be used. In short, if you are using Microsoft 365 services and/or Azure services, KQL is a must-have skill set.
Log analysis
KQL can be used to analyze log data from various sources, such as web servers, applications, and security systems. With KQL, users can quickly search and filter log data to identify potential issues or security threats.
Data analysis
KQL can be used to query and analyze structured data from databases and other data sources. With KQL, users can easily aggregate, filter, and transform data to gain valuable insights and make informed decisions.
Monitoring and alerting
KQL can be used to monitor data sources in real-time and generate alerts when certain conditions are met. This is particularly useful in applications where timely notification of issues or anomalies is critical.
Performance analysis
KQL can be used to monitor and analyze the performance of applications and systems. With KQL, users can quickly identify performance bottlenecks and other issues, and take corrective action to optimize performance.
Getting started with KQL
If you’re new to KQL, Microsoft provides comprehensive documentation and tutorials to help you get started. Here are a few resources to check out:
Kusto Query Language (KQL) documentation
Microsoft’s official documentation for KQL, including syntax, operators, and functions.
https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/
KQL Query Tutorials
Step-by-step tutorials on learn.microsoft.com that teach you how to write KQL queries and perform basic data analysis.