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SQL Server 2017 Integration Services Cookbook

You're reading from   SQL Server 2017 Integration Services Cookbook Powerful ETL techniques to load and transform data from almost any source

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781786461827
Length 558 pages
Edition 1st Edition
Languages
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Authors (6):
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Matija Lah Matija Lah
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Matija Lah
Christo Olivier Christo Olivier
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Christo Olivier
Christian Cote Christian Cote
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Christian Cote
Dejan Sarka Dejan Sarka
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Dejan Sarka
David Peter Hansen David Peter Hansen
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David Peter Hansen
Samuel Lester Samuel Lester
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Samuel Lester
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Toc

Table of Contents (12) Chapters Close

Preface 1. SSIS Setup FREE CHAPTER 2. What Is New in SSIS 2016 3. Key Components of a Modern ETL Solution 4. Data Warehouse Loading Techniques 5. Dealing with Data Quality 6. SSIS Performance and Scalability 7. Unleash the Power of SSIS Script Task and Component 8. SSIS and Advanced Analytics 9. On-Premises and Azure Big Data Integration 10. Extending SSIS Custom Tasks and Transformations 11. Scale Out with SSIS 2017

Introduction

Once the framework is set up, it's time to focus on the different layers of our data warehouse. There are various architectural schools of thought when it comes to data warehouses:

  • Corporate Information Factory (CIF)
  • The Kimball Group dimensional data warehouse
  • Data vault

The main difference between the Kimball Group and the others is the way a datamart is loaded. The Kimball Group approach loads data into a staging area and from there, refreshes the data warehouse. The latter is modeled as a dimensional data warehouse. It is also known as a datamart or star schema. The Kimball Group approach uses denormalized tables in its data warehouse.

A typical data warehouse using the Kimball Group method has the following components:

  • Data sources that can be in different formats such as text files, databases, Excel, and so on
  • A staging area that can be either persistent...
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