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

Introduction

Now, let's go for the real stuff! This chapter will cover many topics that will lay out the foundations of a simple and effective ETL solution. Over the years, I have seen many SSIS implementations and one of the goals of this chapter is to give the readers the following:

  • A simple but effective SSIS framework
  • SSIS development best practices
  • New data source integrations

All remaining chapters assume that we want to load a data warehouse that is a star schema with its staging area.

The source (operational) database used is AdventureWorksLT, an old well-known database. The following diagram describes the source database that we're going to use:

From this database, we'll insert data in a staging area and finally into a data warehouse. The staging area and the data warehouse will be separated in schemas in a database that we'll manage using SSDT.

...