Tag: Cloudera

Feed: CDH – Cloudera Engineering Blog. Author: Cy Jervis. 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). Of particular note, we show that even for a dataset orders of magnitude ... Read More

Feed: Cloud – Cloudera Engineering Blog. Author: Cy Jervis. This article shows how to build and publish a customized Docker image for usage as an engine in Cloudera Data Science Workbench. Such an image or engine customization gives you the benefit of being able to work with your favorite tool chain inside the web based application. Motivation: Cloudera Data Science Workbench (CDSW) enables data scientists to use their favorite tools such as R, Python, or Scala based libraries out of the box in an isolated secure sandbox environment. In a previous blog post I have shown an Apache Maven based ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Justin Kestelyn. Axemblr, purveyors of a cloud-agnostic MapReduce Web Service, have recently announced the availability of an Apache-licensed Java Client for the Cloudera Manager API. The task at hand, according to Axemblr, is to “deploy Hadoop on Cloud with as little user interaction as possible. We have the code to provision the hosts but we still need to install and configure Hadoop on all nodes and make it so the user has a nice experience doing it.” And voila, the answer is Cloudera Manager, with the process made easy via the REST API introduced ... Read More

Feed: Hadoop – Cloudera Engineering Blog. Author: Cy Jervis. 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. On one hand we have veteran frameworks like Theano and Caffe, or more popular ones like TensorFlow, while ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Jon Zuanich. Our video animation factory has been busy lately. The embedded player below contains our two latest ones stitched together: Get Started with Hadoop Using Cloudera Enterprise, Part 1  To be a proactive industry leader, you must be able to consume critical data as it is produced, analyze it quickly and act on your results. Before Hadoop there was no scalable, cost-effective way to do this with Big Data. Learn how Cloudera Enterprise helps you to efficiently store all of your data, avoiding archiving and data loss, and provides the opportunity ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Justin Kestelyn. In this installment of “Meet the Engineers”, meet Jonathan Natkins,  also known as “Natty” by his friends and colleagues.  What do you do at Cloudera, and in which Apache project are you involved? For the last year and a half, I’ve been an engineer on the Enterprise team. We’re the guys who build Cloudera Manager, and all the goodies that make it easy to manage and administer Apache Hadoop clusters. Specifically, I’ve worked on a number of things across the product, like scale and performance for the databases underlying the ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Jon Zuanich. In this demo video, Philip Zeyliger, a software engineer at Cloudera, discusses the Activity Monitoring and Operational Reports in Cloudera Manager. Activity Monitoring The Activity Monitoring feature in Cloudera Manager consolidates all Hadoop cluster activities into a single, real-time view. This capability lets you see who is running what activities on the Hadoop cluster, both at the current time and through historical activity views. Activities are either individual MapReduce jobs or those that are part of larger workflows (via Oozie, Hive or Pig). Activity Monitoring provides many statistics – both ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Jon Zuanich. In this demo video, BC Wong, a software engineer at Cloudera, discusses the Hadoop Service Monitoring feature in Cloudera Manager. Service Monitoring helps you monitor and manage your Hadoop clusters effectively. Through the Service Monitoring feature, customers can monitor dozens of service health and performance metrics about the overall service (HDFS, MapReduce, HBase). They can also examine underlying role instances (Namenode, Datanodes, JobTracker, TaskTrackers, Region Servers etc.) in your Hadoop cluster and see what’s going wrong – or what is about to go wrong. Service Monitoring presents health and performance ... Read More

Feed: Cloudera Manager – Cloudera Engineering Blog. Author: Jon Zuanich. Service and Configuration Management (Part I & II) We’ve recently recorded a series of demo videos intended to highlight the extensive set of features and functions included with Cloudera Manager, the industry’s first end-to-end management application for Apache Hadoop. These demo videos showcase the newly enhanced Cloudera Manager interface and reveal how to use this powerful application to simplify the administration of Hadoop clusters, optimize performance and enhance the quality of service. In the first two videos of this series, Philip Langdale, a software engineer at Cloudera, walks through Cloudera ... Read More

Feed: Cloudera Labs – Cloudera Engineering Blog. Author: Ted Malaska. The SparkOnHBase project in Cloudera Labs was recently merged into the Apache HBase trunk. In this post, learn the project’s history and what the future looks like for the new HBase-Spark module. SparkOnHBase was first pushed to Github on July 2014, just six months after Spark Summit 2013 and five months after Apache Spark first shipped in CDH. That conference was a big turning point for me, because for the first time I realized that the MapReduce engine had a very strong competitor. Spark was about to enter an exciting new ... Read More