Tag Archives: Hadoop

Deep learning on Apache Spark and Apache Hadoop with Deeplearning4j

Categories: Data Science Hadoop Spark

In late 2016, Ben Lorica of O’Reilly Media declared that “2017 will be the year the data science and big data community engage with AI technologies.” Deep learning on GPUs has pervaded universities and research organizations prior to 2017, but distributed deep learning on CPUs is now beginning to gain widespread adoption in a diverse set of companies and domains. While GPUs provide top-of-the-line performance in numerical computing, CPUs are also becoming more efficient and much of today’s existing hardware already has CPU computing power available in bulk.

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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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What’s New in Cloudera Director 2.2?

Categories: CDH Cloud Cloudera Manager Hadoop

This new release adds support for Amazon EBS volumes and the ability to diagnose cluster bootstrap errors quickly.

Cloudera Director provides a simple, reliable, enterprise-grade way to deploy, scale, and manage Apache Hadoop in the cloud of your choice. Cloudera Director enables you to deploy production-ready clusters for big data applications and successfully run workloads in the cloud.

Cloudera Director makes it easier for customers to:

  • Deploy clusters in line with patterns native to cloud infrastructure
  • Use an interface to define in one place the desired cluster specification all the way down to the operating system
  • Repeatedly and programmatically instantiate these cluster definitions
  • Adapt to the dynamic nature of cloud infrastructure

Cloudera Director 2.2 provides additional mechanisms to get that initial cluster definition right and the ability to diagnose errors and iterate quickly.

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Progress Report: Hive-on-Spark Nears Production Readiness

Categories: Cloudera Labs Hive Spark

Contributors from Intel, Cloudera, and the rest of the community have been making strong progress on the Hive-on-Spark initiative. This post provides an update.

[Editor’s note (April 20, 2016): Hive-on-Spark is now GA/shipping starting in CDH 5.7.]

Since its inception about one year ago, the community initiative to make Apache Spark a data processing engine for Apache Hive (HIVE-7292) has attracted widespread interest from developers around the world and gone through phases of rapid development,

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Docker is the New QuickStart Option for Apache Hadoop and Cloudera

Categories: CDH Ops and DevOps QuickStart VM Testing

Now there’s an even quicker “QuickStart” option for getting hands-on with the Apache Hadoop ecosystem and Cloudera’s platform: a new Docker image.

docker-logoYou might already be familiar with Cloudera’s popular QuickStart VM, a virtual image containing our distributed data processing platform. Originally intended as a demo environment, the QuickStart VM quickly evolved over time into quite a useful general-purpose environment for developers, customers, and partners. Today,

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