Microsoft provided the machine learning services in SQL Server so that it can support data science tasks.

    Microsoft provided the machine learning services in SQL Server so that it can support data science tasks. The user need not expose the data unnecessarily so it becomesnonvulnerable while moving it from one platform to another. SQL Server 2016 has R tools for data processing to boost the performance and facilitate scaling of analysis to billions of records. When machine learning is integrated with SQL Server then you can put the R code into production and hence recode is not required.  SQL Server 2017 of Microsoft is launched with the support to Python code. This post is about using R and Python in the database and to inform you about resources to find the solution.

    Nowadays open source data frameworks and the languages have created a new path for machine learning and data analytics through SQL Server. Microsoft brought a mixed version of  Python and R language in its Azure Machine Learning development kit. SQL Server of Microsoft provides myriad functionalities and is not confined only to machine learning. Following are listed five reasons to provide the information about revolution brought through this integration:

    • Support for On-Premise data manipulation, machine learning and visualization
    • Rows and Columns Representation
    • Set Based Operations
    • Set Based Operations
    • Use of T-SQL
    1. Support for On-Premise data manipulation, machine learning, and visualization

    As the R language supports ETL tasks, statistical functions, data visualization and addition of more machine learning packages facilitate other functionalities. The user can create advanced graphics and transform data easily through new and improved machine learning algorithms. If the user has not leveraged R in thecloud, then through Azure ML some cloud-based analysis and modeling can be performed with R in on-premise SQL Server environment.

    1. Rows and Columns Representation

    The R data frameshave two dimension representation and most of the corporate-level analysis is based on the similar representation. Either you are using SQL or Excel as your database you won’t have to wrangle over data frames. A number of advanced data types like lists, matrices, vectors and n-dimensional arrays are available in R and data frame will suit the needs of SQL developers and analysts.

    1. Set Based Operations

    Though functional R code can be written easily using For loops or If and else statements, but the language also has theinbuilt functionality to use the codes across sets or subsets. One can perform such operation either by using one line code through BY function or through various functions of the APPLY family.

    1. Leverage of R community

    The user can explore a number of machine learning algorithm and experience the training and testing models. This feature can only be beneficial for those who have used SSAS, the data mining toolset or the Excel Data Mining Add-ins. Though the SSAS algorithms have covered lots of cases, to implement those algorithms the user has to remain tied to Microsoft’s choices and release schedule.

    In R language the deployment schedule depends on community and user won’t have to wait for Microsoft. Azure ML comes with core functionalities of machine learning and it can be extended as well as an when needed.

    1. Use of T-SQL

    Through SQL Server some of the R functionalities can also be implemented through T-SQL in its “Analytic Library”. A user who does not know R can also leverage its power and can even extend it if they need to. So the advanced analytics can also be performed through R.

    As of now, it can be said that if so far you do not have any plan to learn R, then put it on your learning plan soon due to above-listed reasons. Microsoft is making it featured everyday and many business organizations are leveraging its power. There are a number of offline and online training providers available which provide the R or Python learning e.g. JanBask training. Such training providers provide the training through online mode and can train the students perfectly.

    Wrapping up!

    Nowadays machine learning has become an integral part of the organizations. As R is a machine learning language, it is intimidating for the users and learners. Due to thepresence of complex analytical concepts and functions, a number of organizations are adopting and using it as their data analytical tool. Many users or learners suppose that a background of statistics and linear algebra may be mandatory for learning R, but due to thepresence of many packages and training material, it can be easily learned.


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