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Therefore, it’s now becoming necessary for every organization to exploit the data that they have (or in some cases external data as well) and predict future outcomes to remain competitive by offering their customers what they want, when they want, and where they want.
Looking at this potential, SQL Server 2016 brings native support for doing advanced analytics in the database itself using R Services, without moving data across.
There are multiple ways that data scientists can write and execute R scripts for developing and building predictive models.
Based on preference, they can use any R client tools.
In my last article of the series, I talked about what R is, what on-premise advance analytics options from Microsoft are and how to get started using R Services with SQL Server 2016.
Microsoft IT has the enormous job of rolling out business intelligence (BI) solutions to more than 200,000 people within the company.
It also supports working with subsets of data for greater scalability.
THIS TOPIC APPLIES TO: SQL Server (starting with 2016)Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse This topic describes breaking changes in the SQL Server 2017 Database Engine and earlier versions of SQL Server.
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Every organization wants to use the power of advanced analytics to predict future outcomes and take proactive corrective measures to remain competitive and win over its competitors.
Koulutus soveltuu myös SQL Server 2014 MCSA:lle, jotka haluavat valmistautua päivittämään sertifikaattinsa SQL 2016 -tasolle.