Need to Monitor Any Azure Resource? Must Learn Kusto Query Language
Kusto is a service for storing and running interactive analytics over Big Data. Kusto was designed from scratch to be a “big data” repository for Azure and easy to query using Kusto Query Language (KQL). As we progress in our migration to the cloud, we learn new ways to monitor and alert resources and services. Microsoft has consolidated many services under the umbrella of ‘Azure Monitor.’ Whether you are detecting and diagnose issues across applications and dependencies,’ ‘correlate infrastructure issues with Azure Monitor for VMs,’ ‘drill into your SQL database monitoring data with Log Analytics, you will be using ‘Azure Monitor.’ Azure Monitor uses a version of the KQL used by Azure Data Explorer that is suitable for simple log queries and includes advanced functionality such as aggregations, joins, and intelligent analytics. As we advance, the KQL must be your primary resource for querying the Azure Monitor log.
This 95% demo session will show you some ‘getting started’ tips and a few sophisticated queries using KQL. I will do a live demo, generating an alert using KQL. I will dive into Kqlmagic, which brings you the benefit of notebooks, data analysis, and rich Python capabilities in the same location against Azure Log Analytics workspace. At the end of this session, beginners will have a solid knowledge about KQL that they can build upon by themselves; others will also learn many advanced operators, including machine learning ones. Come and learn about the future of monitoring and investigations of Azure services.
Taiob Ali
Microsoft MVP – Data Platform
Taiob Ali, Microsoft Data Platform MVP, is an accomplished technical leader with a proven record of success. During his last 15 years, he has worked with the Microsoft Data Platform and MongoDB both on-premises and cloud. His experience includes all three major business sectors: finance, e-commerce, and healthcare. Taiob has hands-on experience in managing large database projects, including massive data migration, intricate process design, testing and deployment, performance tuning, long term capacity planning.
