Mastering SQL Server 2017 brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By knowing how to use the features of SQL Server 2017 to your advantage, you can build scalable applications and easily perform data integration and transformation.
After a quick recap of the features of SQL Server 2017, this Learning Path shows you how to use Query Store, columnstore indexes, and In-Memory OLTP in your applications. You'll then learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll learn how to design and build SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. You'll also learn to develop SSIS packages designed to maintain a data warehouse using data flow and other control flow tasks.
By the end of this Learning Path, you'll have the required information to easily design efficient, high-performance database applications. You'll also have explored on-premises big data integration processes to create a classic data warehouse.
This Learning Path includes content from the following Packt products:
- SQL Server 2017 Developer's Guide by Miloš Radivojević, Dejan Sarka, William Durkin
- SQL Server 2017 Integration Services Cookbook by Christian Coté, Dejan Sarka, Matija Lah
