Domino enables better interactive data science with Beaker and Rodeo
Better interactive data science with Beaker and RodeoNick Elprin2019-09-19 | 4 min read Domino has offered support for IPython/Jupyter Notebook for a while, but we recently added support for two newer, up-and-coming tools for interactive data science: Beaker Notebooks, and Rodeo. This post gives a brief overview of each tool and describes how to use them on Domino. Power tools at your fingertipsTh
Overview
BeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, Spark cluster support, polyglot programming, interactive plots, tables, forms, publishing, and more. BeakerX supports: Groovy, Scala, Clojure, Kotlin, Java, and SQL, including many magics; Widgets for time-series plotting, tables, forms, and more (there are Python and JavaSc
Optimizing Spark Machine Learning for Small Data - Eugene Zhulenev
Update 2015-10-08: Optimization “hack” described in this post still works, however we don’t use it in production anymore. With careful parallelism config, overhead introduced by distributed models is negligible. You’ve all probably already know how awesome is Spark for doing Machine Learning on Big Data. However I’m pretty sure no one told you how bad (slow) it can be on Small Data. As I mentioned
Scala as a platform for statistical computing and data science
Darren Wilkinson's blog Statistics, computing, functional programming, data science, Bayes, stochastic modelling, systems biology and bioinformatics Menu and widgets There has been a lot of discussion on-line recently about languages for data analysis, statistical computing, and data science more generally. I don’t really want to go into the detail of why I believe that all of the common choices a
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GitHub - collectivemedia/spark-ext: Spark Extension : ML transformers, SQL aggregations, etc that are missing in Apache Spark
