R language News
November 09, 2017
Python and R are among the tools in the SQL Server machine learning toolkit. Native T-SQL scoring is also on the agenda, as uncovered at PASS Summit 2017.
August 14, 2016
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
June 09, 2016
Spark 2.0, with structured streaming and SQL 2003 support, is aborning as indicated at Databricks' Spark Summit, where R-to-Spark interfaces also popped up.
September 10, 2015
The latest Alteryx analytics engine gets enterprises close to Amazon big data, with nods to spreadsheet aficionados and R language.
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The R programming language is an open source scripting language for predictive analytics and data visualization. Continue Reading
SQL Server 2016 is being released on June 1, 2016. Learn about Stretch Database, PolyBase, changes to In-Memory OLTP, and other new and updated features in this Essential Guide. Continue Reading
Big data analytics is a trending practice that many companies are adopting. Before jumping in and buying big data tools, though, organizations should first get to know the landscape. Continue Reading
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Learn about the features and components of the Microsoft R family of predictive analytics tools, which includes Microsoft R Open, Microsoft R Client and Microsoft R Server. Continue Reading
The SAP BusinessObjects Predictive Analytics suite of on-premises and cloud-based tools can be used by data scientists, analytics professionals, as well as less technical teams. Continue Reading
The SAS Enterprise Miner data mining tool helps users develop descriptive and predictive models, including components for predictive modeling and in-database scoring. Continue Reading
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What does the R analytics language offer to users, and how does it fit into SQL Server 2016 and 2017? Get answers to those questions and details on Microsoft's various R technologies. Continue Reading
Big data initiatives can help companies improve operational efficiency, create new revenue and gain a competitive advantage. But traditional data processing often can't deal with the mountains of structured, semi-structured and unstructured data that needs to be mined for value. That leaves big data initiatives hungry for new tools and technologies to ease and speed data processing and predictive analytics functions.
In this e-book, get insight on useful tools for big data projects. The first chapter provides real-world examples of organizations using SQL-on-Hadoop engines to simplify the process of querying and analyzing Hadoop data. The second defines Spark -- including its capabilities and limitations -- and offers advice on deploying, managing and using the big data processing engine. And the third chapter focuses on using the open source R analytical programming language and commercial tools such as SAS and IBM SPSS to run analytical applications against Hadoop data sets.Continue Reading
Open source data visualization technologies have matured to the point where users say the available tools can handle large amounts of their visualization workloads. Continue Reading