This blog post will detail what KQL is all about…
KQL was developed to take advantage of the power of the cloud through clustering and compute. Using this capability, KQL is designed as a well-performing tool to help surface critical data quickly. This a big part of why it works so well and outshines many other query languages like it. KQL was built for the cloud and to be used against large data sets.
Kusto Query Language is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more. The query uses schema entities that are organized in a hierarchy similar to SQL’s: databases, tables, and columns.
The query we write in KQL is a read-only request to process data and return results. The request is stated in plain text, using a data-flow model that is easy to read, author, and automate. Kusto queries are made of one or more query statements.
Here is an example query:
StormEvents
| where StartTime between (datetime(2007-11-01) .. datetime(2007-12-01))
| where State == "FLORIDA"
| count
I will show you below how to run this code and also any code that I put in these blog posts – for FREE!!
Where did I first discover Kusto Query Language?
In Azure using Log Analytics – as I was collecting diagnostic data and needed to analyse it:
Where can you discover and try out Kusto Query Language?
Well Microsoft have some free demo sites you can use:
•Log Analytics https://aka.ms/LADemo
•Application Insights https://aka.ms/AIAnalyticsDemo
But it gets better….
Go here to run the code above and get the result of “28”
https://dataexplorer.azure.com/clusters/help/databases/Samples
You don’t need to pay anything just have an account registered with Microsoft.
It’s that easy…
#Yip.