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

Designing patterns to load dimensions of a data warehouse

The difference between these patterns is the way historical data is stored in the dimensions. We call them Slowly Changing Dimensions (SCD). The following points give an overview of various SCD types:

  • Type 0: This retains the original. This means that any changes to a specific member of the dimension will result in a new member inserted with new values. As opposed to SCD type 2, there's no concept of the current version or start and end date of a row. This SCD type is rarely used.
  • Type 1: This overwrites changes, no history is kept. For example, let's say we have a person's marital status attribute in a claimant dimension. If the initial value at insertion was Single, the attribute value is updated to Married when the person gets married.
  • Type 2: This keeps history (versioning). A bunch of system columns are added to the dimension:
    • The start and end date of the dimension member (row). Usually, the start date equals the date when the...