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
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Data cleansing with DQS


In this recipe, you will create a view with some dirty data and use a DQS cleansing project to cleanse it. You will use the DQS knowledge base prepared in the previous exercise.

Getting ready

This recipe assumes that you have built the DQS knowledge base from the previous recipe. In addition, you need to prepare some demo data in advance. In SSMS, use the following query to prepare the data:

USE DQS_STAGING_DATA;
SELECT C.CustomerKey,
C.FirstName + ' ' + c.LastName AS FullName,
C.AddressLine1 AS StreetAddress,
G.City, G.StateProvinceName AS StateProvince,
G.EnglishCountryRegionName AS CountryRegion,
C.EmailAddress, C.BirthDate,
C.EnglishOccupation AS Occupation
INTO dbo.CustomersCh05
FROM AdventureWorksDW2014.dbo.DimCustomer AS C
INNER JOIN AdventureWorksDW2014.dbo.DimGeography AS G
ON C.GeographyKey = G.GeographyKey
WHERE C.CustomerKey % 10 = 0;
GO

How to do it...

  1. The data prepared in the previous section is clean. For the DQS cleansing project, use the following code...