You're reading from Hands-On SQL Server 2019 Analysis Services Design and query tabular and multi-dimensional models using Microsoft's SQL Server Analysis Services

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800204768

Length 474 pages

Edition 1st Edition

Languages

SQL

Tools

SQL Server

Concepts

Data Analysis

Author (1):

Steven Hughes

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Table of Contents (19) Chapters

Preface

1. Section 1: Choosing Your Model

2. Chapter 1: Analysis Services in SQL Server 2019 FREE CHAPTER

3. Chapter 2: Choosing the SQL Server 2019 Analytic Model for Your BI Needs

4. Section 2: Building and Deploying a Multidimensional Model

5. Chapter 3: Preparing Your Data for Multidimensional Models

6. Chapter 4: Building a Multidimensional Cube in SSAS 2019

7. Chapter 5: Adding Measures and Calculations with MDX

8. Section 3: Building and Deploying Tabular Models

9. Chapter 6: Preparing Your Data for Tabular Models

10. Chapter 7: Building a Tabular Model in SSAS 2019

11. Chapter 8: Adding Measures and Calculations with DAX

12. Section 4: Exposing Insights while Visualizing Data from Your Models

13. Chapter 9: Exploring and Visualizing Your Data with Excel

14. Chapter 10: Creating Interactive Reports and Enhancing Your Models in Power BI

15. Section 5: Security, Administration, and Managing Your Models

16. Chapter 11: Securing Your SSAS Models

17. Chapter 12: Common Administration and Maintenance Tasks

18. Other Books You May Enjoy

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Reviewing other maintenance tasks or tools

In this section, we have a number of tasks and tools that can support the operational needs of your SQL Server Analysis Server solutions. Let's look at each of them in detail.

Warming multidimensional models

One of the issues with multidimensional models is that they use caching extensively to improve query performance. When a cube is reprocessed, the cache is cleaned up. When users start to use the cube, they can experience significant performance issues because the data is being retrieved from disk as opposed to memory. While this can happen at any time, especially when uncommon queries are run, it can be frustrating if, every Monday morning, the CEO needs to wait for this query. Once it is cached, it performs great.

The solution to this issue is to warm the cache. This typically involves running several queries right after the cube has been processed to load common or key data into the cache, thereby improving the user experience...