![]() The latter option enables SQL IntelliSense, syntax highlighting, and deployment. To take full advantage of SQL Server integration, use the Visual Studio installer to install the Data storage and processing workload with the SQL Server Data Tools option. Wide availability at no additional cost: Language integrations are available in all editions of SQL Server 2017 and later, including the Express edition. Installing a package in SQL Server is as simple as installing a package on your local machine. Rich extensibility: You can install and run any of the latest open source packages in SQL Server to build deep learning and AI applications on huge amounts of data in SQL Server. The elimination of data movement also means that you avoid client memory constraints as your data grows or you wish to increase the performance of the application. No specific language integrations are necessary.Įnterprise-grade performance and scale: You can use SQL Server’s advanced capabilities like in-memory table and column store indexes with the high-performance scalable APIs in the RevoScale packages. ![]() Any SQL client application written in any language can then take advantage of the models and intelligence through a stored procedure call. You can also consume datasets that couldn't fit into the memory of a client machine.Įasy deployment: Once you have a model ready, deploying it to production is a simple matter of embedding it in a T-SQL script. This capability eliminates barriers of security, compliance, governance, integrity, and a host of similar issues related to moving vast amounts of data around. You enjoy the following advantages by running your code where your data already lives:Įlimination of data movement: Instead of moving data from the database to your application or model, you can build applications in the database. ![]() Python support is available in SQL Server 2017 CTP 2.0 and later. SQL Server supports using Python to do advanced analytics directly inside SQL Server. NET language that's suited for a wide variety of data processing tasks.)īy default, the workload installs the following options, which you can modify in the summary section for the workload in the Visual Studio installer: ![]() Those packages address a wide range of scenarios such as data acquisition, cleaning, model training, deployment, and plotting. Python is easy to learn and is supported by a rich ecosystem of packages. Python is a primary scripting language used for data science. ![]()
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