Kusto Query Language (KQL) from Scratch
KQL, the Kusto Query Language, is used to query Azure's services. This course will teach you the basic syntax of KQL, then cover advanced topics such as machine learning and time series analysis, as well as exporting your data to various platforms.
What you'll learn
Increasingly, Azure is becoming the infrastructure backbone for many corporations. It is imperative then, that you have the ability to query Azure into gain insights to the Azure services your company is using. In this course, Kusto Query Language (KQL) from Scratch, you will learn foundational knowledge to query a variety of Azure services. First, you will learn the basics of KQL, the Kusto Query Language. Next, you will progress to advanced KQL abilities such as machine learning and time series analysis. Finally, you will explore how to export the results of your KQL queries to CSV files and PowerBI. When you're finished with this course, you will have the skills and knowledge of the Kusto Query Language needed to gain valuable insights into your Azure services.
Table of contents
- The print and now Commands 3m
- The ago Command 3m
- The sort Command 3m
- The extract Command 3m
- The parse Command 3m
- datetime and timespan Arithmetic 4m
- The startof... Commands 4m
- The endof... Commands 1m
- The between Commands 4m
- The todynamic Command 5m
- The format_datetime and format_timespan Commands 6m
- The datetime_part Command 3m
- The iif Command 3m
- The case Command 3m
- The isempty and isnull Commands 4m
- The split Command 4m
- String Operators 2m
- The strcat Command 6m
- The arg_max and arg_min Commands 3m
- The makeset and makelist Commands 3m
- The mvexpand Command 2m
- The percentiles Command 3m
- The dcount Command 4m
- The dcountIf Command 3m
- The countif Command 2m
- The pivot Command 1m
- The top-nested Command 4m
- The max and min Commands 1m
- The sum and sumif Commands 1m
- The any Command 3m
Course FAQ
Kusto Query Language (KQL) is a read only request to process data and return results.
Machine learning is the study of computer algorithms that improve automatically through experience and time.
In this course, you will learn about scaler operators and aggregations, how to work with datasets, machine learning, time series analysis. By the end of this course, you will be comfortable writing KQL queries.
Prerequisites for this course are a familiarity with Azure and and understanding of basic query concepts.
Some of the benefits of big data are: better decision making, increased efficiency, cuts costs, key insights, and increased predicting capabilities.
