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Hands-On SQL Server 2019 Analysis Services

Hands-On SQL Server 2019 Analysis Services

By : Steven Hughes
5 (6)
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Hands-On SQL Server 2019 Analysis Services

Hands-On SQL Server 2019 Analysis Services

5 (6)
By: Steven Hughes

Overview of this book

SQL Server Analysis Services (SSAS) continues to be a leading enterprise-scale toolset, enabling customers to deliver data and analytics across large datasets with great performance. This book will help you understand MS SQL Server 2019’s new features and improvements, especially when it comes to SSAS. First, you’ll cover a quick overview of SQL Server 2019, learn how to choose the right analytical model to use, and understand their key differences. You’ll then explore how to create a multi-dimensional model with SSAS and expand on that model with MDX. Next, you’ll create and deploy a tabular model using Microsoft Visual Studio and Management Studio. You'll learn when and how to use both tabular and multi-dimensional model types, how to deploy and configure your servers to support them, and design principles that are relevant to each model. The book comes packed with tips and tricks to build measures, optimize your design, and interact with models using Excel and Power BI. All this will help you visualize data to gain useful insights and make better decisions. Finally, you’ll discover practices and tools for securing and maintaining your models once they are deployed. By the end of this MS SQL Server book, you’ll be able to choose the right model and build and deploy it to support the analytical needs of your business.
Table of Contents (19 chapters)
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1
Section 1: Choosing Your Model
4
Section 2: Building and Deploying a Multidimensional Model
8
Section 3: Building and Deploying Tabular Models
12
Section 4: Exposing Insights while Visualizing Data from Your Models
15
Section 5: Security, Administration, and Managing Your Models

Discovering multidimensional model strengths and challenges

When multidimensional models were introduced, they were difficult for most report developers to wrap their heads around. They were not relational in nature and we had to think differently about structures. In order to create a good multidimensional model, we now need to understand dimensional modeling and denormalization (we dive into details on this in Chapter 3, Preparing Your Data for Multidimensional Models). However, the results were high performing, ad hoc-capable analytics databases that were simple for users to consume.

This section will focus on the strengths and challenges specific to multidimensional models. While not necessarily an exhaustive list, these strengths and challenges will influence your model choice. These are not listed in any particular order because they may apply differently to your specific technical or business needs.

Strengths of the multidimensional model

In this section, we will discuss...

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