On Wednesday, Microsoft showed off a series of new tools and services aimed at helping companies bridge gap between their on-premise SQL databases and its Azure cloud database offerings.
The tools, shown off at the PASS Summit in Seattle, are designed to help companies manage Azure cloud databases, SQL Server 2017 databases, and combinations of the two in hybrid setups, as well an update to the Power BI data analysis tool.
The aim of the updates, says Microsoft Database Systems Engineering general manager Rohan Kumar, is a familiar refrain among cloud database vendors: create a powerful cloud-only service while also maintaining support for companies that want to stick with their on-premises systems, either partially or entirely.
Among the new tools are two services Microsoft is testing in private previews, a 'lift and shift tool' called Azure Database Migration Service that, as its name implies, helps move on-prem SQL, MySQL and Oracle Database servers into Azure instances. Also in private preview is Managed Azure SQL database, a cloud instance designed to be managed closely with on-premise SQL.
Redmond also gave a short preview of Microsoft SQL Operations Studio, a tool out in the next few weeks for making databases using SQL Server, Azure SQL Database, and Azure SQL Data Warehouse on Windows, Mac or Linux machines.
Kumar told The Register that in addition to the technical hurdles for managing cloud, on-prem and hybrid systems, Microsoft has also found a challenge in convincing companies and developers who have grown familiar with SQL server to embrace new features, such as integration with machine-learning tools, that have been offered in Azure and hybrid databases.
"We are going from static queries to machine learning and AI, how do you make that seamless to the developers" Kumar said. "How do you make that journey where what you have to learn is close to minimal?"
To help integrate some of those tools, Microsoft said it put features like integration with Python and R scripts into SQL Server 2017.
This, it is hoped, will simplify the process of integrating machine learning libraries with SQL and also allow developers and researchers to work directly in SQL Server, rather than force them to migrate, while also letting SQL Server take advantage of the GPU processing many Python-based AI scripts employ. ®