As data professionals, we often ask ourselves: Why yet another new coding language. In the release of Fabric the KQL (Kusto Query Language) was also a part of the need to have a full implementation capability.

When we already have Python, PySpark, Scala and T-SQL? What problems does KQL solve, and when is it the right tool for the job vs. T-SQL?

In the world of data, choosing the right tool for the job can be the key to success. Both languages are powerful, and they are designed for different purposes and platforms. Understanding their strengths, differences, and ideal use cases can make or break your project when working with diverse data ecosystems.

Through a comparative discussion, we’ll give you the knowledge to know the strengths of each.

The foundational differences between T-SQL and KQL: syntax, execution, and purpose.
Ideal scenarios for using T-SQL versus KQL.
Key features like joins, aggregations, and data transformations, and how they are implemented in each language.
Practical use cases, including transitioning between the two when working in hybrid systems.
Tips and tricks on how you can use your T-SQL skills in the KQL world.

Whether you’re a T-SQL professional curious about the capabilities of KQL, or a KQL enthusiast looking to expand your database coding skills, this session will provide valuable insights to bridge the gap between these two powerful languages.

Let’s explore the best of both worlds and equip you with the knowledge of choosing the right tool for the right job in your projects.