Book Image

Mastering SQL Server 2017

By : Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah
Book Image

Mastering SQL Server 2017

By: Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah

Overview of this book

Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You’ll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you’ll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Miloš Radivojevi?, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al
Table of Contents (20 chapters)
Title Page
Free Chapter
1
Introduction to SQL Server 2017

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...