Start getting hands on with KQL using Azure Data Explorer’s free cluster offering.
OK, you’ve answered the question What is KQL?, read the primer, understand you need to know KQL (especially if you are working with Microsoft services) and want to start dabbling. While you can start working with KQL queries directly against live data sources such as Azure Log Analytics, Application Insights, or Microsoft Defender, where you can you start working with a data sandbox and cultivating your KQL skills?
Azure Data Explorer free cluster tier
Microsoft offers a free cluster tier of Azure Data Explorer to anyone with a Microsoft account (personal or work/school). It is a great way to get started with loading data and analyzing it with KQL.
ADX free cluster limitations
There are some limitations on the free cluster (outlined below), however they are pretty generous for a free personal sandbox.
| Item | Value |
|---|---|
| Storage (uncompressed) | ~ 100 GB |
| Databases | Up to 10 |
| Tables per database | Up to 100 |
| Columns per table | Up to 200 |
| Materialized views per database | Up to 5 |
Additionally, the free cluster does not have ALL the features of a full ADX cluster, however for our learning purposes it will be sufficient. For a full feature comparison, check out learn.microsoft.com:
https://learn.microsoft.com/en-us/azure/data-explorer/start-for-free#feature-comparison
Sign up for your free Azure Data Explorer cluster
To get signed up:
- Go to https://dataexplorer.azure.com/freecluster.
- Sign in with your personal Microsoft account or an Azure AD account (typically a work or school account).
- Complete the form picking the cluster location closest to you.
- Click Sign in and create cluster.
Once your new free cluster has been created, you’ll be redirected to the cluster main page.
Next week: learning Azure Data Explorer
That’s it for a Friday; upcoming posts will cover learning the basics of Azure Data Explorer before diving into using ADX for data analysis with KQL. If you can’t wait, try clicking Home in ADX and then click Explore sample data with KQL!