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Attention all SQL Pros, DAX is not just for writing Excel-based formulas! Get hands-on learning and expert advice on how to use the vast capabilities of the DAX language to solve common data modeling challenges. Beginning DAX with Power BI teaches key concepts such as mapping techniques from SQL to DAX, filtering, grouping, joining, pivoting, and using temporary tables, all aimed at the SQL professional. Join author Philip Seamark as he guides you on a journey through typical business data transformation scenarios and challenges, and teaches you, step-by-step, how to resolve challenges using DAX. Tips, tricks, and shortcuts are included and explained, along with examples of the SQL equivalent, in order to accelerate learning. Examples in the book range from beginner to advanced, with plenty of detailed explanation when walking through each scenario. What You’ll Learn: Turbocharge your Power BI model by adding advanced DAX programming techniques Know when to use calculated measures versus calculated columns Generate new tables on the fly from existing data Optimize, monitor, and tune Power BI to improve performance of your models Discover new ideas, tricks, and time-saving techniques for better models This book is for business intelligence developers, business analysts, or any SQL user who wants to use Power BI as a reporting tool. A solid understanding of SQL is recommended, as examples throughout the book include the DAX equivalents to SQL problem/solution scenarios. Philip Seamark,a Senior BI architect with Trade Me and a Microsoft Data Platform MVP with more than 25 years of commercial experience delivering business applications across multiple technologies. He is an experienced database and BI professional with a deep understanding of the Microsoft stack and extensive knowledge of data warehouse (DW) methodologies and enterprise data modeling. Philip writes for the Power BI community site, and is a speaker at many industry events, Microsoft Ignite, Data Insights, Power BI User Groups, SQL Saturdays, and more.

Business --- Data processing. --- Computer science. --- Big data. --- Computer Science. --- Microsoft and .NET. --- Big Data/Analytics. --- Electronic data processing --- Microsoft software. --- Microsoft .NET Framework. --- Computer software --- Data sets, Large --- Large data sets --- Data sets