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

View More author details

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

Leave a review - let other readers know what you think

Data optimization considerations

Another consideration when preparing your data for tabular models is the data refresh options available. Typically, data is imported into your tabular model similar to the process we used with multidimensional models. Imported data is loaded into memory and optimized by the VertiPaq engine. This involves a high level of compression, including columnar data storage techniques. The functions of compression and memory combine to create an optimized model with performance. Here are some key considerations when using data refresh:

Refresh frequency: The data is only as fresh as the last import. If the data source has been updated recently, the data may be out of sync. This is less of an issue when you are loading data from a data warehouse. The data warehouse is typically loaded in batches as well. If you match your refreshes to the batch loads, your data will be consistent with the data warehouse. If you have chosen to use the transactional database...