Scalable Data Science with Spark, R, RStudio, & sparklyr

Presented by

Nanda Vijaydev, Director of Solutions Management, BlueData; and Anant Chintamaneni, Vice President, Products, BlueData

About this talk

Watch this on-demand webinar to learn how to get started with large-scale distributed data science. Do your data science teams want to use R with Spark to analyze large data sets? How do you provide the flexibility, scalability, and elasticity that they need – from prototyping to production? In this webinar, we discussed how to: -Evaluate compute choices for running R with Spark (e.g., SparkR or RStudio Server with sparklyr) -Provide access to data from different sources (e.g., Amazon S3, HDFS) to run with R and Spark -Create on-demand environments using Docker containers, either on-premises or in the cloud -Improve agility and flexibility while ensuring enterprise-grade security, monitoring, and scalability Find out how to deliver a scalable and elastic platform for data science with Spark and R.

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