The ABCs of ADX: Learning the Basics of Azure Data Explorer | Data Exposed: MVP Edition

You may have heard of Azure Data Explorer - but do you know what it does? Do we know the best ways to use it (and the ways we shouldn't use it)? Do we know some things that it does better than anything else in the Microsoft data platform? Join us for a walkthrough of what Azure Data Explorer is, what it isn't, and how to leverage it to offer your customers, colleagues, and users another tool in their data toolbox.
Chapters:
00:00 - Introduction
03:23 - Demo
12:56 - Azure Data Explorer in Fabric
✔️ Resources:
Learning path: learn.microsoft.com/en-us/tra...
Help Cluster with built-in test data: dataexplorer.azure.com/cluste...
About Matt Gordon:
Matt is a Microsoft Data Platform MVP and has worked with the Microsoft data platform since 2000. He is the leader of the Lexington, KY Data Technology Group and a frequent domestic and international community speaker. His role as a Senior Architect at Centric Consulting allows him to help customers make the right data platform decisions to gather relevant insights and harness the power of the cloud and AI to give their users and consumers the best experience with their platforms and tools.
About MVPs:
Microsoft Most Valuable Professionals, or MVPs, are technology experts who passionately share their knowledge with the community. They are always on the "bleeding edge" and have an unstoppable urge to get their hands on new, exciting technologies. They have very deep knowledge of Microsoft products and services, while also being able to bring together diverse platforms, products and solutions, to solve real world problems. MVPs make up a global community of over 4,000 technical experts and community leaders across 90 countries/regions and are driven by their passion, community spirit, and quest for knowledge. Above all and in addition to their amazing technical abilities, MVPs are always willing to help others - that's what sets them apart. Learn more: aka.ms/mvpprogram
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[@bobbytables6629]

My question is why create KQL in the first place, why new lang, I mean you could of even used some of the DAX lang to do the same. I just don't get why everyone should learn a new flavor of syntax.