Thanks to Michal Malohlava, Amy Wang, and Avni Wadhwa of H20.ai for providing the following guest post about building ML apps using Sparkling Water and Apache Spark on CDH.
The Sparkling Water project is nearing its one-year anniversary, which means Michal Malohlava, our main contributor, has been very busy for the better part of this past year. The Sparkling Water project combines H2O machine-learning algorithms with the execution power of Apache Spark.
Wrangle, a new conference dedicated to the practice of data science from startup to enterprise, debuts in San Francisco on Oct. 22, 2015.
Even as Cloudera introduce new tools for analytics and machine learning into its platform (like the recently announced Ibis project, for example), we are mindful of the fact that many of the hardest problems in data science cannot be solved by technology alone. From the smallest startups to the largest enterprises,
This new Cloudera Labs project promises to deliver the great Python user experience and ecosystem at Hadoop scale.
Across the user community, you will find general agreement that the Apache Hadoop stack has progressed dramatically in just the past few years. For example, Search and Impala have moved Hadoop beyond batch processing, while developers are seeing significant productivity gains and additional use cases by transitioning from MapReduce to Apache Spark.
Thanks to such advances in the ecosystem,
Set up your own, or even a shared, environment for doing interactive analysis of time-series data.
Although software engineering offers several methods and approaches to produce robust and reliable components, a more lightweight and flexible approach is required for data analysts—who do not build “products” per se but still need high-quality tools and components. Thus, recently, I tried to find a way to re-use existing libraries and datasets stored already in HDFS with Apache Spark.
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,