Tag Archives: data

How to Distribute your R code with sparklyr and Cloudera Data Science Workbench

Categories: CDH How-to Spark

sparklyr is a great opportunity for R users to leverage the distributed computation power of Apache Spark without a lot of additional learning. sparklyr acts as the backend of dplyr so that R users can write almost the same code for both local and distributed calculation over Spark SQL.

 

Since sparklyr v0.6, we can run R code across our Spark cluster with spark_apply().

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How To Predict ICU Mortality with Digital Health Data, DL4J, Apache Spark and Cloudera

Categories: CDH Data Science Spark

Modeling EHR Data in Healthcare

In this case study, we take a look at modeling electronic health record (EHR) data with deep learning and Deeplearning4j (DL4J). We draw inspiration from recent research showing that carefully designed neural network architectures can learn effectively from the complex, messy data collected in EHRs. Specifically, we describe how to train an  long short-term memory recurrent neural network (LSTM RNN) to predict in-hospital mortality among patients hospitalized in the intensive care unit (ICU).

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Deep Learning with Intel’s BigDL and Apache Spark

Categories: CDH Data Science Hadoop Spark

Cloudera recently published a blog post on how to use Deeplearning4J (DL4J) along with Apache Hadoop and Apache Spark to get state-of-the-art results on an image recognition task. Continuing on a similar stream of work, in this post we discuss a viable alternative that is specifically designed to be used with Spark, and data available in Spark and Hadoop clusters via a Scala or Python API.

The Deep Learning landscape is still evolving.

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Introducing Apache HBase Medium Object Storage (MOB) compaction partition policies

Categories: HBase

Introduction

The Apache HBase Medium Object Storage (MOB) feature was introduced by HBASE-11339. This feature improves low latency read and write access for moderately-sized values (ideally from 100K to 10MB based on our testing results), making it well-suited for storing documents, images, and other moderately-sized objects [1]. The Apache HBase MOB feature achieves this improvement by separating IO paths for file references and MOB objects, applying different compaction policies to MOBs and thus reducing write amplification created by HBase’s compactions.

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Common Probability Distributions: The Data Scientist’s Crib Sheet

Categories: Data Science

Data scientists have hundreds of probability distributions from which to choose. Where to start?

Data science, whatever it may be, remains a big deal.  “A data scientist is better at statistics than any software engineer,” you may overhear a pundit say, at your local tech get-togethers and hackathons. The applied mathematicians have their revenge, because statistics hasn’t been this talked-about since the roaring 20s. They have their own legitimizing Venn diagram of which people don’t make fun.

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