Thanks to Matthew Dixon, principal consultant at Quiota LLC and Professor of Analytics at the University of San Francisco, and Mohammad Zubair, Professor of Computer Science at Old Dominion University, for this guest post that demonstrates how to easily deploy exposure calculations on Apache Spark for in-memory analytics on scenario data.
Since the 2007 global financial crisis, financial institutions now more accurately measure the risks of over-the-counter (OTC) products. It is now standard practice for institutions to adjust derivative prices for the risk of the counter-party’s,
Authored by a substantial portion of Cloudera’s Data Science team (Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills), Advanced Analytics with Spark (currently in Early Release from O’Reilly Media) is the newest addition to the pipeline of ecosystem books by Cloudera engineers. I talked to the authors recently.
Why did you decide to write this book?
We think it’s mostly to fill a gap between what a lot of people need to know to be productive with large-scale analytics on Apache Hadoop in 2015,
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.
The ability to quickly and accurately count complex events is a legitimate business advantage.
In our work as data scientists, we spend most of our time counting things. It is the foundational skill that is used in data cleansing, reporting, feature engineering, and simple-but-effective machine learning models like Naive Bayes classifiers. Hilary Mason has a quote about the benefits of counting that I love:
Understand that what big data really means is to be able to count things in data sets of any size,
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,