Building the Right Environment to Support AI, Machine Learning and Deep Learning
The launch of a release candidate is generally an indicator that a product is complete and the final bugs are being worked out. This week Microsoft made available SQL Server 2017 RC1, which can be downloaded now. You can find SQL Server 2017 downloads for Linux, macOS, Windows, Docker and Azure on the Microsoft site at:
Some of the features added to SQL Server 2017 include support for Linux at higher levels, graph data processing, adaptive query processing, in-database analytics using Python or R, active Directory Authentication support, enhancements to Machine Learning Services , Dynamic Management Views in SQL Server Analysis Services (SSAS), Always on for SQL Server Integration Services (SSIS) on Windows Server, and more.
One of the goals for the new product was to have SQL Server 2017 run on any public or private cloud infrastructure. With the support for containers, SQL Server 2017 is able to better support Continuous Integration/Continuous Deployment (CI/CD) scenarios. This all fits within the objective of positioning SQL Server to work for DevOps.
If you are working in a DevOp environment with SQL Server, then there are a number of tools you’ll be able to tap into from Microsoft. This includes the Visual Studio SQL Server Data Tools for doing the CI/CD as well as using the Redgate Data Tools that Microsoft had aligned with recently. You can also use the msql-scripter to generate scripts for database objects in SQL Server as well as in Azure data stores. The sqlcmd is now available for Linux, Windows, and the macOS so you can enter commands and such at the command prompt. Finally, bcp is also available on Linux, Windows and the macOS to copy files in specific formats.
The following is a Channel9 video showing some of the SQL Server Data Tools that you’ll be able to use in the DevOps pipeline: