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