Alberto Ferrari is a Business Intelligence consultant. Alberto helped several software houses to build complex BI solutions, from the OLTP to the final OLAP cubes, providing planning hints all over the development lifecycle. He is a book author also, having worked on "Exper Cube Development with Microsoft SQL Server 2008" with Marco Russo and Chris Webb and Microsoft PowerPivot for Excel 2010: Give Your Data Meaning with Marco Russo.
During this session we are going to analyze common business problems that require and advanced usage of DAX functions and data modeling with PowerPivot.
Deep dive into the handling of many to many relationships in DAX. How to make them work and how to optimize their speed thorugh many patterns and live examples of M2M usage.
We are going to show how to compute classical time intelligence with the built-in DAX functions. Then, we will show some more complex time intelligence formulas that require to think out of the box, using advanced data modeling techniques.
In this session, we will analyze the way DAX solves filtering. Starting from simple queries, we will follow the steps DAX does with the filter context, discovering the internals of the query engine of DAX.
A deep dive in the internals of the database architecture, discovering how Vertipaq stores information, in order to gain better insights into the engine and understand the best way to model your data warehouse to leverage the features of VertiPaq.
Alberto will start with a simple query and he will perform on stage all the necessary steps to optimize it, showing you the tools ant the techniques used to identify the bottleneck and to fix the performance issues
This session goes beyond the classical star schema modeling, exploring new techniques to model data with Power Pivot and SSAS Tabular. You will see how brute-force power in DAX allows different data models than those used in SSAS Multidimensional
Alberto will show you some common techniques to use when building a budget model with Power Pivot and Power Query, including previous year allocation, multiple-step budgeting with linked back tables, handling of budget on non-existing products
Tabular offers only one-to-many relationships on a single column: it seems poor when compared with Multidimensional. However, with DAX, you can handle any relationship. We analyze and solve scenarios with calculated, virtual and complex many-to-many.
In this session we will show you how Tabular performs when you are querying a model with many billions rows, conduct a complete analysis of the model searching for optimization ideas and implement them on the fly, so to look at the effect of using th