Book Image

Hands-On SQL Server 2019 Analysis Services

By : Steve Hughes
Book Image

Hands-On SQL Server 2019 Analysis Services

By: Steve 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)
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

Prepping data for tabular models

With multidimensional models, a star schema is required in the underlying data source. However, with tabular models, a star schema is not required. This means that data preparation is not as clear as it is with multidimensional models. In this section, we will explore some key considerations that are involved when preparing data for tabular models.

Contrasting self-service and managed deployments

Tabular model designs have their origins in self-service technologies such as Power BI and Excel. Why does this matter? Because well-designed dimensional models still perform better and are easier to develop solutions for. Self-service models often focus only on the immediate business need and not on lasting performance or growth. When the number of consumers of an analytics model is one or just a few, the impact is minimal. However, when scaling the models beyond a limited set of users, performance and usability become key considerations in design.

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