Analyzing data with KQL

With SQL Server, a user will interact with data using T-SQL; with Spark, they will use Scala, PySpark, or SparkSQL, and with a KQL database, they will use KQL. This is an extremely powerful yet straightforward and easy-to-learn language that will allow you to explore data. Just like other query languages, KQL uses a database context along with tables and columns to identify the data to query.

The structure of a query is slightly different from many of the common SQL languages because it starts with a table followed by operators that accept a tabular input and return a tabular output, which can then be returned to the user or passed to the next operator for further refinement. A few examples of operators are where, summarize, union, and join.

Note

The entirety of the KQL language is case-sensitive, including table names, column names, and operators.

With a few basics about query structure out of the way, let’s dive into analyzing the stock...