Author Archives: Justin Kestelyn

How-to: Install Cloudera Enterprise on Microsoft Azure (Part 1)

Categories: Cloud Guest How-to

Recently, GoDataDriven installed a Cloudera Enterprise (CDH + Cloudera Manager) cluster on Microsoft Azure. This two-part series (republished with permission) includes information about use case, design, and installation.

Processing large amounts of unstructured data requires serious computing power and also maintenance effort. As load on computing power typically fluctuates due to time and seasonal influences and/or processes running on certain times, a cloud solution like Microsoft Azure is a good option to be able to scale up easily and pay only for what is actually used.

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Progress Report: Bringing Erasure Coding to Apache Hadoop

Categories: Hadoop HDFS Performance

Get an update on the progress of the effort to bring erasure coding to HDFS, including a report about fresh performance benchmark testing results.

About a year ago, the Apache Hadoop community began the HDFS-EC project to build native erasure coding support inside HDFS (currently targeted for the 2.9/3.0 release). Since then, we have designed and implemented basic functionalities in the first phase of the project under HDFS-7285,

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YCSB 0.6.0 Update from Cloudera Labs

Categories: Cloudera Labs Performance

A new Cloudera Labs release of YCSB includes a variety of usability improvements.

A few months ago, this blog post announced that the YCSB framework is now a Cloudera Labs project. YCSB is the popular standard for evaluating the performance of a variety of data-serving systems and NoSQL stores such as Apache HBase and Apache Cassandra.

Since that time, the reinvigorated YCSB development community has been very active and produced multiple releases that incorporate several valuable improvements.

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How-to: Train Models in R and Python using Apache Spark MLlib and H2O

Categories: Data Science How-to Spark

Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity.

There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you’ll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name).

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How-to: Design An Analytic Database Schema on Apache Impala (Incubating) with Indyco

Categories: Guest How-to Impala

Our thanks to Manuel Spezzani, Indyco Technical Leader, and Edward William Gnudi, Indyco’s Chief of Customer Happiness, for the guest post below about using Indyco alongside Apache Impala.

In this post, you will learn how to automatically design a complete data warehouse solution on top of Impala using Indyco, a tool for designing, exploring, and understand your business model (recently named Cloudera Certificated Partner for the Impala platform).

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