Book Image

SQL Server 2017 Integration Services Cookbook

By : Christian Cote, Dejan Sarka, David Peter Hansen, Matija Lah, Samuel Lester, Christo Olivier
Book Image

SQL Server 2017 Integration Services Cookbook

By: Christian Cote, Dejan Sarka, David Peter Hansen, Matija Lah, Samuel Lester, Christo Olivier

Overview of this book

SQL Server Integration Services is a tool that facilitates data extraction, consolidation, and loading options (ETL), SQL Server coding enhancements, data warehousing, and customizations. With the help of the recipes in this book, you’ll gain complete hands-on experience of SSIS 2017 as well as the 2016 new features, design and development improvements including SCD, Tuning, and Customizations. At the start, you’ll learn to install and set up SSIS as well other SQL Server resources to make optimal use of this Business Intelligence tools. We’ll begin by taking you through the new features in SSIS 2016/2017 and implementing the necessary features to get a modern scalable ETL solution that fits the modern data warehouse. Through the course of chapters, you will learn how to design and build SSIS data warehouses packages using SQL Server Data Tools. Additionally, you’ll learn to develop SSIS packages designed to maintain a data warehouse using the Data Flow and other control flow tasks. You’ll also be demonstrated many recipes on cleansing data and how to get the end result after applying different transformations. Some real-world scenarios that you might face are also covered and how to handle various issues that you might face when designing your packages. At the end of this book, you’ll get to know all the key concepts to perform data integration and transformation. You’ll have explored on-premises Big Data integration processes to create a classic data warehouse, and will know how to extend the toolbox with custom tasks and transforms.
Table of Contents (18 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback


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 (contains all history of data loaded) or transient (emptied every time data is loaded)
  • One or more datamarts that are tied to...