Category Archives: Spark

Bayesian Machine Learning on Apache Spark

Categories: Data Science General Spark

Markov Chain Monte Carlo methods are another example of useful statistical computation for Big Data that is capably enabled by Apache Spark.

During my internship at Cloudera, I have been working on integrating PyMC with Apache Spark. PyMC is an open source Python package that allows users to easily apply Bayesian machine learning methods to their data, while Spark is a new, general framework for distributed computing on Hadoop. Together,

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How-to: Use IPython Notebook with Apache Spark

Categories: How-to Spark

IPython Notebook and Spark’s Python API are a powerful combination for data science.

[Editor’s note (added April 25,2016): See updated docs on this subject here.]

The developers of Apache Spark have given thoughtful consideration to Python as a language of choice for data analysis. They have developed the PySpark API for working with RDDs in Python, and further support using the powerful IPythonshell instead of the builtin Python REPL.

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New in CDH 5.1: Apache Spark 1.0

Categories: CDH Spark

Spark 1.0 reflects a lot of hard work from a very diverse community.

Cloudera’s latest platform release, CDH 5.1, includes Apache Spark 1.0, a milestone release for the Spark project that locks down APIs for Spark’s core functionality. The release reflects the work of hundreds of contributors (including our own Diana Carroll, Mark Grover, Ted Malaska, Colin McCabe, Sean Owen, Hari Shreedharan, Marcelo Vanzin, and me).

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New Apache Spark Developer Training: Beyond the Basics

Categories: Spark Training

While the new Spark Developer training from Cloudera University is valuable for developers who are new to Big Data, it’s also a great call for MapReduce veterans.

When I set out to learn Apache Spark (which ships inside Cloudera’s open source platform) about six months ago, I started where many other people do: by following the various online tutorials available from UC Berkeley’s AMPLab, the creators of Spark. I quickly developed an appreciation for the elegant,

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Estimating Financial Risk with Apache Spark

Categories: Data Science Spark Use Case

Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.

Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations,

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