Category Archives: CDH

A Look at ADLS Performance – Throughput and Scalability

Categories: CDH Cloud Hadoop HDFS Performance

Overview

Azure Data Lake Store (ADLS) is a highly scalable cloud-based data store that is designed  for collecting, storing and analyzing large amounts of data, and is ideal for enterprise-grade applications.  Data can originate from almost any source, such as Internet applications and mobile devices; it is stored securely and durably, while being highly available in any geographic region.  ADLS is performance-tuned for big data analytics and can be easily accessed from many components of the Apache Hadoop ecosystem,

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Informatica Big Data Management on Cloudera Altus

Categories: CDH Cloud

Today, we’re really excited to announce the latest innovation from Cloudera and Informatica’s partnership. Companies are increasingly moving their data operations into the cloud. With both companies focusing on helping customers derive business insights out of vast amounts of data, our new joint offering will dramatically simplify leveraging cloud-native infrastructures for big data analytics.

Last May, Cloudera announced Cloudera Altus, a new platform-as-a-service (PaaS) offering in the cloud for big data analytics,

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Cloudera SDX: Under the Hood

Categories: CDH

What is SDX?

Shared Data Experience — SDX — is Cloudera’s secret ingredient that makes it possible to deploy Cloudera’s four core functions (Data Engineering, Data Science, Analytic DB, Operational DB) on a single platform.

Why does that matter?

First, each of those core functions is essential to any modern enterprise business.

  • Data Engineering enables the business to run batch or stream processes that speed ETL and train machine learning models
  • Data Science enables the business to do exploratory data science at big data scale with full data security and governance
  • Analytic DB delivers the fastest time-to-insight with the flexibility and agility to run in any environment and against any type of data.

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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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