Cloudera Engineering Blog · Guest Posts
A World-Class EDW Requires a World-Class Hadoop Team
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Vagrant is a very nice tool for programmatically managing many virtual machines (VMs) on a single physical machine. It natively supports VirtualBox and also provides plugins for VMware Fusion and Amazon EC2, supporting the management of VMs in those environments as well.
As a follow-up to a previous post about the Impala demo he built during Data Hacking Day, Alan Gardner from Pythian has deployed the app for a limited time on Amazon EC2. We republish his original post below.
A little while ago I blogged about (and open sourced) a Cloudera Impala-powered soccer visualization demo, designed to demonstrate just how responsive Impala queries can be. Since not everyone has the time or resources to run the project themselves, we’ve decided to host it ourselves on an EC2 instance. [Note: instance live only for one week!] You can try the visualization; we’ve also opened up the Impala web interface, where you can see query profiles and performance numbers, and Hue (username and password are both ‘test’), where you can run your own queries on the dataset.
Deploying Impala on EC2
The following FAQ is provided by James Taylor of Salesforce, which recently open-sourced its Phoenix client-embedded JDBC driver for low-latency queries over HBase. Thanks, James!
What is this new Phoenix thing I’ve been hearing about?
Phoenix is an open source SQL skin for HBase. You use the standard JDBC APIs instead of the regular HBase client APIs to create tables, insert data, and query your HBase data.
The following guest post comes from Alejandro Caceres, president and CTO of Hyperion Gray LLC – a small research and development shop focusing on open-source software for cyber security.
Imagine this: You’re an informed citizen, active in local politics, and you decide you want to support your favorite local political candidate. You go to his or her new website and make a donation, providing your bank account information, name, address, and telephone number. Later, you find out that the website was hacked and your bank account and personal information stolen. You’re angry that your information wasn’t better protected — but at whom should your anger be directed?
The following guest post is provided by Aaron Kimball, CTO of WibiData.
The Kiji ecosystem has grown with the addition of a new module, KijiMR. The Kiji framework is a collection of components that offer developers a handle on building Big Data Applications. In addition to the first release, KijiSchema, we are now proud to announce the availability of a second component: KijiMR. KijiMR allows KijiSchema users to use MapReduce techniques including machine-learning algorithms and complex analytics to develop many kinds of applications using data in KijiSchema. Read on to learn more about the major features included in KijiMR and how you can use them.
This guest post is provided by Rohit Menon, Product Support and Development Specialist at Subex.
I am a software developer in Denver and have been working with C#, Java, and Ruby on Rails for the past six years. Writing code is a big part of my life, so I constantly keep an eye out for new advances, developments, and opportunities in the field, particularly those that promise to have a significant impact on software engineering and the industries that rely on it.
In my current role working on revenue assurance products in the telecom space for Subex, I have regularly heard from customers that their data is growing at tremendous rates and becoming increasingly difficulty to process, often forcing them to portion out data into small, more manageable subsets. The more I heard about this problem, the more I realized that the current approach is not a solution, but an opportunity, since companies could clearly benefit from more affordable and flexible ways to store data. Better query capability on larger data sets at any given time also seemed key to derive the rich, valuable information that helps drive business. Ultimately, I was hoping to find a platform on which my customers could process all their data whenever they needed to. As I delved into this Big Data problem of managing and analyzing at mega-scale, it did not take long before I discovered Apache Hadoop.
Mission: Hands-On Hadoop
This blog was originally published at blog.apache.org/pig and is republished here for your convenience by permission of its author, Pig Committer Dmitriy Ryaboy.
After months of work, we are happy to announce the 0.11 release of Apache Pig. In this blog post, we highlight some of the major new features and performance improvements that were contributed to this release. A large chunk of the new features was created by Google Summer of Code (GSoC) students with supervision from the Apache Pig PMC, while the core Pig team focused on performance improvements, usability issues, and bug fixes. We encourage CS students to consider applying for GSOC in 2013 – it’s a great way to contribute to open source software.
This was post was originally published by U.C. Berkeley AMPLab developer (and former Clouderan) Matt Massie, on his personal blog. Matt has graciously permitted us to re-publish here for your convenience.
Note: The post below is valid for Impala version 0.6 only and is not being maintained for subsequent releases. To deploy Impala 0.7 and later using a much easier (and also free) method, use this how-to.
Introduction: Training is Key
Apache Hadoop is extremely important to maximizing the value Syncsort’s technology delivers to our customers. That value promise starts with a solid foundation of knowledge and skills among key technical staff across the company.