R Server for HDInsight
Predictive analytics, machine learning, and statistical modeling for big data
What is R Server for HDInsight?
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
- Large portable R parallel analytics and machine learning library
- Terabyte-scale machine learning—1,000x larger than in open source R
- Deliver up to 50x faster performance using R Server for Apache Spark 2.0 and optimized vector/math libraries
- Enterprise-grade security and support backed by a Microsoft SLA
- Access Spark data sources through Spark SQL
- Easy setup for fast results
Work with the power and familiarity of R
A top choice among data scientists, the R programming language has a global community of more than two million users worldwide, and the total number of open-source analytics packages is growing every year. R Server for HDInsight gives you full compatibility with the R language running at scale on Hadoop and Spark.
Large portable R parallel analytics and machine learning library
Take advantage of a large parallel analytics and machine learning library, built to work with the open-source R language, that’s portable across popular data platforms—including decision trees and ensembles, regression models, clustering, data preparation, visualization, and statistical functions.
Terabyte-scale machine learning handles 1,000x more data
With transparent parallelization on top of Hadoop and Spark, R Server for HDInsight lets you handle terabytes of data—1,000x more than the open source R language alone. Train logistic regression models, trees, and ensembles on any amount of data. You’re only limited by the size of your Spark cluster.
Get up to 50x faster performance
Combine Spark, multithreaded vector and matrix math libraries, and R Server for HDInsight to experience up to 50x faster performance than previously possible with open source R.
Run distributed parameter sweeps and simulations with existing R functions
Run any open source R function over hundreds of nodes for parallel parameter sweeps and simulations. Explore and refine your models for faster, easier, and more accurate predictions.
Access Spark data sources through Spark SQL
Analyze data in Hadoop and Spark, using Apache Spark SQL as a data source for R Server. Load the results of a Spark SQL query against sources such as Apache Hive and Apache Parquet to a Spark Data Frame, and analyze it directly using any R Server distributed computing algorithms.
Choose your development tools
R Server on HDInsight includes R Studio Server Community Edition, which makes it easy for you to get started. Download R Tools for Visual Studio for free to get a convenient local development environment.
Enterprise-grade security and support
Rely on enterprise-grade security and support from Azure, which includes version packages, patching, security updates, and continuous cluster monitoring. A Microsoft Service Level Agreement (SLA) with 99.9% connectivity helps to protect your R Server for HDInsight clusters against catastrophic events.
Easy setup, fast results
There’s no time-consuming installation or setup with R Server for HDInsight. Azure does it for you. You’ll be up and running in minutes, ready to train your statistical and machine learning models, without buying new hardware or paying other up-front costs. You only pay for the compute and storage that you use.
Why trust R Server for HDInsight?
Microsoft invests more than USD 1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts who are dedicated to data security and privacy.
Azure has more certifications than any other cloud provider. View the comprehensive list.