Just a few weeks ago, I was accepted into the IDERA ACE program class of 2020! Oh, never heard of it? Well, let me clue you in…
IDERA ACEs are active community members who have shown a passion for helping the community and sharing their knowledge. Members are selected annually and are up-and-coming data professionals who have shown a passion for teaching and wish to become more active in the community. This program helps active members of the database and data modeling community share industry knowledge by sponsoring their travel for speaking engagements and networking opportunities for a variety of SQL and Data Management conferences and events.
In short, as a member of the IDERA ACE program, I get to travel to industry conferences like SQL Saturday, PASS Summit, and the like – for FREE!
(Well not totally free – just travel expenses such as hotel/flight – but still pretty darn good as these are likely conferences I’d likely be going to anyways. And as an independent consultant, travel expenses for stuff like this typically comes directly out of my pocket – now they’ll come out of IDERA’s pocket, so yeah, free money!)
So expect to see me out there, making the rounds on the SQL Saturday circuit, talking about Power BI, Analysis Services, DAX, and the future of Data Warehousing in the coming months. As a matter of fact, your first opportunity will be at SQL Saturday Charlotte on December 7th 2019 where I’ll be delivering the following session:
What You Need To Know About Processing Tabular Models and Power BI Datasets
Ever wondered what’s happening under the covers when processing a tabular model or a Power BI dataset? Why is it taking so much memory? How can I make it run faster? Am I running into a resource bottleneck?
Processing a tabular model can be a very resource-intensive workload and depending on your specific goals (e.g. speed, availability, etc) it may not always be feasible to process the entire model.
In this session, you’ll learn what actually happens and in what order during model processing, the difference between the various processing types, performance considerations and most common resource bottlenecks. We’ll also cover the most common processing patterns and the associated trade-offs.
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