What is Kusto Query Language?
Being able to query large data sets is a good skill to have, whether you work in security, IT operations, or data analytics. This is where Kusto Query Language or KQL can help. In this blog, we'll take a look into what KQL is, where it's used, and how its components help you get the most out of your data. What is KQL Kusto Query Language (KQL) was developed by Microsoft in 2017. It was first introduced with Azure Data Explorer. KQL is a read only language. Which means it is used solely for querying data and not for modifying or deleting data. This makes KQL particularly powerful and safe for data exploration. It also means that when you are learning or using the language your queries can’t inadvertently alter your data. Since its first introduction KQL has become widely used among a number of Azure services. You’ll find it being used in the following products: Azure Data Explorer (ADX): The primary platform for KQL, used for big data analytics and interactive data exploration. Azure Monitor : For monitoring the performance and health of applications and infrastructure. Log Analytics : A part of Azure Monitor, it uses KQL to analyse log and telemetry data from various sources. Application Insights : Also part of Azure Monitor, it uses KQL to monitor and diagnose performance issues in applications. Microsoft Sentinel : A security information and event management (SIEM) system that uses KQL to analyse security data and detect threats. Microsoft Defender for Cloud : Uses KQL to provide security insights and threat detection for cloud resources. Azure Graph Explorer: allows users to efficiently explore and query their Azure resources across multiple subscriptions and resource groups, providing comprehensive visibility. There is a wide community that supports and shares knowledge around KQL. It’s not just a tool for data professionals, it’s used by Security professionals, and IT pros alike. Basic components of KQL To use KQL it’s important to understand some of the basic components. Operators Operators are essential to KQL’s functionality, they enable users to filter, project, summarize and join data, just to name a few. Functions Functions in KQL are predefined commands that perform specific operations on the data. These functions allow users to perform things such as calculations, and transformations on their datasets. Examples of functions are: Count : to count the number of rows. Avg : to calculate average values. Max : to find maximum values. Min : to find minimum values. Clauses Clauses are segments of a query that defines specific actions or conditions. They are critical to structuring KQL queries and ensuring that the desired data is retrieved. The most common clause is the where clause. The where filter puls out data from the dataset that matches the criteria given. Conclusion Kusto Query Language (KQL) is an invaluable tool for querying and analysing lots of different types of data in Azure. Its read-only nature ensures that data remains intact, making it both a safe and reliable option for exploration and troubleshooting. Whether you're just starting out or you're looking to deepen your knowledge, KQL is a skill worth mastering. Explore its capabilities, engage with the active community, and begin leveraging KQL to gain deeper insights into your data.
Being able to query large data sets is a good skill to have, whether you work in security, IT operations, or data analytics. This is where Kusto Query Language or KQL can help.
In this blog, we'll take a look into what KQL is, where it's used, and how its components help you get the most out of your data.
What is KQL
Kusto Query Language (KQL) was developed by Microsoft in 2017. It was first introduced with Azure Data Explorer.
KQL is a read only language. Which means it is used solely for querying data and not for modifying or deleting data. This makes KQL particularly powerful and safe for data exploration. It also means that when you are learning or using the language your queries can’t inadvertently alter your data.
Since its first introduction KQL has become widely used among a number of Azure services. You’ll find it being used in the following products:
- Azure Data Explorer (ADX): The primary platform for KQL, used for big data analytics and interactive data exploration.
- Azure Monitor : For monitoring the performance and health of applications and infrastructure.
- Log Analytics : A part of Azure Monitor, it uses KQL to analyse log and telemetry data from various sources.
- Application Insights : Also part of Azure Monitor, it uses KQL to monitor and diagnose performance issues in applications.
- Microsoft Sentinel : A security information and event management (SIEM) system that uses KQL to analyse security data and detect threats.
- Microsoft Defender for Cloud : Uses KQL to provide security insights and threat detection for cloud resources.
- Azure Graph Explorer: allows users to efficiently explore and query their Azure resources across multiple subscriptions and resource groups, providing comprehensive visibility.
There is a wide community that supports and shares knowledge around KQL. It’s not just a tool for data professionals, it’s used by Security professionals, and IT pros alike.
Basic components of KQL
To use KQL it’s important to understand some of the basic components.
Operators
Operators are essential to KQL’s functionality, they enable users to filter, project, summarize and join data, just to name a few.
Functions
Functions in KQL are predefined commands that perform specific operations on the data. These functions allow users to perform things such as calculations, and transformations on their datasets. Examples of functions are:
- Count : to count the number of rows.
- Avg : to calculate average values.
- Max : to find maximum values.
- Min : to find minimum values.
Clauses
Clauses are segments of a query that defines specific actions or conditions. They are critical to structuring KQL queries and ensuring that the desired data is retrieved.
The most common clause is the where clause. The where filter puls out data from the dataset that matches the criteria given.
Conclusion
Kusto Query Language (KQL) is an invaluable tool for querying and analysing lots of different types of data in Azure. Its read-only nature ensures that data remains intact, making it both a safe and reliable option for exploration and troubleshooting.
Whether you're just starting out or you're looking to deepen your knowledge, KQL is a skill worth mastering. Explore its capabilities, engage with the active community, and begin leveraging KQL to gain deeper insights into your data.
