Alberto Ferrari

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Alberto started working with SQL Server in 2000 and immediately his interest focused on Business Intelligence. He and Marco Russo created sqlbi.com, where they publish extensive content about Business Intelligence.
Alberto published several books about Analysis Services, Power BI, and Power Pivot. He is a Microsoft MVP and he earned the SSAS Maestro title, the highest level of certification on Microsoft Analysis Services technology.
Today, Alberto's main activities are in the delivery of DAX and data modeling workshops for Power BI and Analysis Services all around the world. Alberto offers consulting services on large and complex data warehouses to provide assessments and validation of project analysis or to perform specific problem-solving activities.
Alberto is a well-known speaker at many international conferences, like PASS Summit, Sqlbits, and Microsoft Ignite. He loves to be on stage both at large events and at smaller user groups meetings, exchanging ideas with other SQL and BI fans. When traveling for work, he likes to engage with local user groups to provide evening sessions about his favorite topics. Thus, you can easily meet Alberto by looking up local Power BI user groups during scheduled courses.
Outside of SQLBI, most of Alberto's personal time is spent practicing video games, in the vain hope of eventually beating his son.
Alberto Ferrari has submitted 6 sessions for SQLBits 2020, although the agenda hasn't been chosen yet. See all submitted sessions.

Pending Sessions

Improve youd Power BI reports with DAX
We show a collection of useful DAX formulas to improve the quality of reports by mixing the power of visuals and DAX.
Using calculation groups in DAX
This session introduces calculation groups, when they should be used and several examples of use cases. You will also learn how calculation groups work internally and what are the limits of this amazing tool.
DirectQuery, Aggregations, Vertipaq: when to use them?
Analysis and comparison of a model with DirectQuery (with and without aggreations) and in VertiPaq mode. We show the speed of the engines and guide students through an educated choice of the best techology
Improve youd Power BI reports with DAX
We show a collection of useful DAX formulas to improve the quality of reports by mixing the power of visuals and DAX.
Using calculation groups in DAX
This session introduces calculation groups, when they should be used and several examples of use cases. You will also learn how calculation groups work internally and what are the limits of this amazing tool.
DirectQuery, Aggregations, Vertipaq: when to use them?
Analysis and comparison of a model with DirectQuery (with and without aggreations) and in VertiPaq mode. We show the speed of the engines and guide students through an educated choice of the best techology

Sessions

Previous Sessions

PowerPivot Advanced Data Modeling
During this session we are going to analyze common business problems that require and advanced usage of DAX functions and data modeling with PowerPivot.
Many-to-Many Relationships in DAX
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.
Time Intelligence in DAX
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.
DAX Query Engine Internals
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.
Inside xVelocity InMemory Engine (VertiPaq)
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.
Optimizing & Analyzing DAX Query Plans
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
Advanced Modeling with Analysis Services Tabular
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
Budgeting with Power Pivot
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
Handling Relationships in DAX
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.
Optimizing multi-billion row tables in Tabular
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
DAX Optimization Examples
In this session we analyze some DAX expressions and Tabular models and, through the usage of DAX Studio and some understanding of the VertiPaq model, we will look at how to optimize them.
Understanding relationships in Power BI
Relationships are present in any data model. Multiple relationships between the same tables and bidirectional filters increase the complexity of this topic. In this session, we discover the complexity behind relationships in complex models.
Aggregations in Power BI
We introduce the concept of aggregation, we show several examples of their usage understanding the advantages and the limitations of aggregations, with the goal of building a solid understanding on how and when to use the feature in data models.
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