Category Archives: Data Science

Accelerating Apache Spark MLlib with Intel® Math Kernel Library (Intel® MKL)

Categories: Data Science Spark

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).

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Analyzing US flight data on Amazon S3 with sparklyr and Apache Spark 2.0

Categories: CDH Data Science Hadoop Spark Use Case

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,

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Up and running with Apache Spark on Apache Kudu

Categories: CDH Data Ingestion Data Science General Hadoop How-to Impala Kudu Spark Training Use Case

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,

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How-to: Automate Your sparklyr Environment with Cloudera Director

Categories: Cloudera Manager Data Science Hadoop How-to Ops and DevOps Spark

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.

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How-to: Do Scalable Graph Analytics with Apache Spark

Categories: Data Science Graph Processing How-to Spark

Get started with scalable graph analysis via simple examples that utilize GraphFrames and Spark SQL on HDFS.

Graphs—also known as “networks”—are ubiquitous across web applications. As a refresher, a graph consists of nodes and edges. A node can be any object, such as a person or an airport, and an edge is a relation between two nodes, such as a friendship or an airline connection between two cities.

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