Work with temporal tables to access information stored in a table at any time
Get familiar with the latest features in SQL Server 2017 Integration Services
Program and extend your packages to enhance their functionality
Description
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
What you will learn
Use columnstore indexes to make storage and performance improvements
Extend database design solutions using temporal tables
Exchange JSON data between applications and SQL Server
Migrate historical data to Microsoft Azure by using Stretch Database
Design the architecture of a modern Extract, Transform, and Load (ETL) solution
Implement ETL solutions using Integration Services for both on-premise and Azure data
Christian Cote is an IT professional with more than 15 years of experience working in a data warehouse, Big Data, and business intelligence projects. Christian developed expertise in data warehousing and data lakes over the years and designed many ETL/BI processes using a range of tools on multiple platforms. He's been presenting at several conferences and code camps. He currently co-leads the SQL Server PASS chapter. He is also a Microsoft Data Platform Most Valuable Professional (MVP).
Milo Radivojevi is a data platform MVP and specializes in SQL Server for application developers and performance/ query tuning. Milo is a co-founder of PASS Austria.
William Durkin is a data platform architect for Data Masterminds, he is a regular speaker at conferences around the globe, a Data Platform MVP, and the founder of the popular SQLGrillen event.
Matija Lah has more than 18 years of experience working with Microsoft SQL Server, mostly from architecting data-centric solutions in the legal domain. His contributions to the SQL Server community have led to him being awarded the MVP Professional award (Data Platform) between 2007 and 2017/2018. He spends most of his time on projects involving advanced information management and natural language processing, but often finds time to speak at events related to Microsoft SQL Server where he loves to share his experience with the SQL Server platform.
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