In this post, I will demonstrate the usage of the k-means clustering algorithm in R and in Apache Spark.Apache Spark (hereinafter Spark) offers two implementations of k-means algorithm: one is packaged with its MLlib library; the other one exists in Spark’s spark.ml package. While both implementations are currently more or less functionally equivalent, the Spark

In this post we will review the more important aspects related to RDD checkpointing. We will continue working on the over500 RDD we created in the previous post on caching. You will remember that checkpointing is a process of truncating an RDD’s lineage graph and saving its materialized version on a persistence store.

Spark offers developers two simple and quite efficient techniques to improve RDD performance and operations against them: caching and checkpointing. Caching allows you to save a materialized RDD in memory, which greatly improves iterative or multi-pass operations that need to traverse the same data set over and over again (e.g. in machine learning algorithms.)

Last week I completed development of our 2 day class teaching Apache Spark which will be integrated in our Big Data and Data Science classes after the QA cycle. I will be feeding some fragments of the material with additional comments and notes that would help you get a taste of what the new content is all

Spark added support for R back in version 1.4.1. and you can use it in Spark Standalone mode. Big Hadoop distros, like Cloudera’s CDH and Hortonworks’ HDP that bundle Spark, have varying degree of support for R. For the time being, CDH decided to opt out of supporting R (their latest CDH 5.8.x version does