Cloudera Engineering Blog · Guest Posts
Our thanks to Brian Dirking, Director of Product Marketing for Alteryx, for the guest post below:
At Alteryx we are excited about the release of Cloudera Impala. The impact on Big Data Analytics is that the ability to perform real-time queries on Apache Hadoop will provide faster access and results. This is applicable to our customers, the business users who are running analytics to get access to data, perform analytics, and then follow up with new questions. Insight doesn’t happen all at once. The ability to query and refine quickly is ultimately what will lead business users to insight.
Our thanks to Ted Wasserman, product manager for Tableau, for the guest post below:
Many of our customers are turning to Apache Hadoop as they grapple with their big data challenges. Hadoop offers many benefits such as its scalability, economics, and versatility. Even so, adoption-to-date has largely centered around applications with “batch”-oriented workloads because of the latency imposed by the MapReduce framework. To increase Hadoop’s usefulness and adoption in the business intelligence space where users need fast, interactive response times when they ask a question, a new approach was needed.
Our thanks to Yves de Montcheuil, Vice President of Marketing for Talend, for the guest post below:
According to Wikipedia, the impala is a medium-sized African antelope; its name comes from the Zulu language meaning “gazelle”. Like elephants, it is found in savannas, and this may be the link with Hadoop. Impala is also the name of Cloudera’s SQL-on-Apache Hadoop project, launched in beta at Strata last October and just released in version 1.0.
A World-Class EDW Requires a World-Class Hadoop Team
Persado is the global leader in persuasion marketing technology, a new category in digital marketing. Our revolutionary technology maps the genome of marketing language and generates the messages that work best for any customer and any product at any time. To assure the highest quality experience for both our clients and end-users, our engineering team collaborates with Ph.D. statisticians and data analysts to develop new ways to segment audiences, discover content, and deliver the most relevant and effective marketing messages in real time.
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.