Book Image

Tabular Modeling with SQL Server 2016 Analysis Services Cookbook

By : Derek Wilson
Book Image

Tabular Modeling with SQL Server 2016 Analysis Services Cookbook

By: Derek Wilson

Overview of this book

SQL Server Analysis Service (SSAS) has been widely used across multiple businesses to build smart online analytical reporting solutions. It includes two different types of modeling for analysis services: Tabular and Multi Dimensional. This book covers Tabular modeling, which uses tables and relationships with a fast in-memory engine to provide state of the art compression algorithms and query performance. The book begins by quickly taking you through the concepts required to model tabular data and set up the necessary tools and services. As you learn to create tabular models using tools such as Excel and Power View, you’ll be shown various strategies to deploy your model on the server and choose a query mode (In-memory or DirectQuery) that best suits your reporting needs. You’ll also learn how to implement key and newly introduced DAX functions to create calculated columns and measures for your model data. Last but not least, you’ll be shown techniques that will help you administer and secure your BI implementation along with some widely used tips and tricks to optimize your reporting solution. By the end of this book, you’ll have gained hands-on experience with the powerful new features that have been added to Tabular models in SSAS 2016 and you’ll be able to improve user satisfaction with faster reports and analytical queries.
Table of Contents (18 chapters)
Tabular Modeling with SQL Server 2016 Analysis Services Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


This chapter will focus on how to modify and enhance the model built in the previous chapter. After building a model, we will need to maintain and enhance the model as the business users update or change their requirements. We will begin by adding additional tables to the model that contain the descriptive data columns for several code columns. Then we will create relationships between these new tables and the existing data tables.

Once the new data is loaded into the model, we will modify various pieces of the model, including adding a new key performance indicator.

Next, we will perform calculations to see how to create and modify measures and columns.