The emergence of “Big Data” has made machine learning much easier because the key burden of statistical estimation—generalizing well to new data after observing only a small amount of data—has been considerably lightened. In a typical machine learning task, the goal is to design the features to separate the factors of variation that explain the observed data. However, a major source of difficulty in many real-world artificial intelligence applications is that many of the factors of variation influence every single piece of data we can observe.
There are two clear trends in the big-data ecosystem: the growth of machine learning use cases that leverage large distributed data sets, and the growth of Spark’s Machine Learning libraries (often referred to as MLlib) for these use cases. In fact, Spark’s MLlib library is arguably the leading solution for machine learning on large distributed data sets.
Intel and Cloudera have collaborated to speed up Spark’s ML algorithms, via integration with Intel’s Math Kernel Library (Intel® MKL).
We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R.
If you are interested in sparklyr, you can learn how to use it with the official document,
After the GA of Apache Kudu in Cloudera CDH 5.10, we take a look at the Apache Spark on Kudu integration, share code snippets, and explain how to get up and running quickly, as Kudu is already a first-class citizen in Spark’s ecosystem.
As the Apache Kudu development team celebrates the initial 1.0 release launched on September 19, and the most recent 1.2.0 version now GA as part of Cloudera’s CDH 5.10 release,
Since the launch of sparklyr, working with Apache Spark in Apache Hadoop has become much easier for R users. sparklyr contains a dplyr interface into Spark and allows users to leverage crucial machine learning algorithms from Spark MLlib and H2O Sparkling Water. This greatly reduces the barrier of entry for R users in adopting Spark as a tool for big data and should go a long way in enabling R workloads to migrate to Hadoop.