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 warehouses packages using SQL server data tools. You'll also learn 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 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.
- Use columnstore indexes to get 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-premises and Azure data
All of the code is organized into folders. Chapter1, Chapter7, Chapter8, Chapter9, Chapter12 and Chapter14 do not contain code files.
For an optimal student experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 8 GB RAM
- Storage: 40 GB available space
You'll also need the following software installed in advance:
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
- Browser: Google Chrome, Latest Version
- SQL Server 2017 Developer or Enterprise Edition.
- SQL Server Management Studio
- Other tools you may need are Visual Studio 2015, SQL Data Tools 16 or higherand SQL Server Management Studio 17 or later.
- You will need Hortonworks Sandbox Docker for Windows Azure account and Microsoft Azure.
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