Book Image

Microsoft Tabular Modeling Cookbook

By : Paul te Braak
Book Image

Microsoft Tabular Modeling Cookbook

By: Paul te Braak

Overview of this book

Business Intelligence Semantic Models (BISM) is a technology that is designed to deliver analytical information to users through a variety of mechanisms that include model structure, definition, and design. This book demonstrates how to create BISM models so that information can be presented to users in an intuitive and easy-to-use format. Once the model is defined, we also show you how it can be managed and maintained so that the data in it remains current and secure. Microsoft Tabular Modeling Cookbook is an all-encompassing guide to developing, managing, creating, and using analytical models using the Business Intelligence Semantic Model (BISM). This title covers a range of modeling situations and common data analysis related problems to show you the techniques required to turn data into information using tabular modeling. Microsoft Tabular Modeling Cookbook examines three areas of tabular modeling: model development, model management and maintenance, and reporting. This book is a practical guide on how to develop semantic models and turn business data into information. It covers all phases of the model lifecycle from creation to administration and finally reporting. It also shows you how to create models which are designed to analyze data. All sections of BISM modeling from development to management and finally reporting are covered. The sections on development examine a wide range of techniques and tricks required to build models, including moving data into the model, structuring the model to manipulate the data, and finally the formulas required to answer common business questions; all of these are discussed in this book in detail. Finally, the book examines methods of reporting on the data within the model, including the creation of data-driven workbooks and reports for a powerful end user experience.
Table of Contents (18 chapters)
Microsoft Tabular Modeling Cookbook
About the Author
About the Reviewers

Using projection to combine data from different tables

The evaluation of an entire table (as shown in the previous recipe, Retrieving data from a single table) excludes the wider concept of projection, because the example returns all the columns from a single table. More often than not, we wish to return only a subset of columns (or perhaps even derived columns) from one or more tables. Unlike SQL, which allows projection in its syntax, DAX has no succinct projection equivalent.

Consider the following SQL statement, which selects column_a from table_a and column_b from table_b.

Select table_a.column_a, table_b.column_b From …

Using DAX, we cannot specify a projection in the same manner (as follows):

Evaluate('table_a'[column_a], 'table_b'[column_b])

If we wish to mimic this activity using DAX, we must define the table as part of the evaluate statement. This recipe examines how to do that.

Getting ready

This recipe shows the concepts of projection by answering a common type of question (from our...