You're reading from Hands-On SQL Server 2019 Analysis Services

Product type Book

Published in Oct 2020

Publisher Packt

ISBN-13 9781800204768

Pages 474 pages

Edition 1st Edition

Languages

Concepts

Data Analysis

Author (1):

Steve Hughes

Table of Contents (19) Chapters

Preface

1. Section 1: Choosing Your Model

2. Chapter 1: Analysis Services in SQL Server 2019

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

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Loading data into your star schema

Now that you have your star schema designed, what's next? You need to build a plan so that you can load data from the source systems and get them properly in place in your star schema. In this section, I will give you some pointers to get you going. This is not an exhaustive or complete loading strategy but should help you understand the basics you need to plan.

Staging your data

I recommend that you plan to stage your data in a database prior to loading it into your star schema. The process of lookups and standardization can add significant load on the system that you are loading from. By having a staging database or a staging schema, you can load data from various source systems with minimal transformation. Then, using the resources dedicated to the process, the data can be loaded from staging to the star schema without there being any negative impacts on the source. How often you stage data can be managed based on the needs of the source...