Discovering Gold with Big Data Analytics and Data-Intensive Computing

Slidecast: TwinStrata CloudArray – Disaster Recovery as a Service

 

In this slidecast, Nicos Vekiarides from TwinStrata presents: TwinStrata CloudArray 4.5 with DRaaS. The new offering is an on-demand disaster recovery as a service (DRaaS) for VMware users.

Whether your goals are to increase storage capacity, improve off-site data protection, implement disaster recovery or all three of the above, TwinStrata CloudArray is the most comprehensive storage solution available today,” said Nicos Vekiarides, CEO of TwinStrata. “TwinStrata has made great strides in delivering enterprise-class functionality at a fraction of the cost typically required of storage solutions. What’s exciting is CloudArray 4.5 enables organizations to enjoy a full business continuity plan without the need for backup software or a dedicated disaster site– a once unthinkable proposition.”

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Big Data and the Elephant in the Room

 

Over at the Actian Blog, Jim Harris has posted an interesting review of the new book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel.

In the book, Siegel says that the big secret about Big Data is that it doesn’t really exist. What is big today will be dwarfed by what is coming.

Everything is connected to everything else—if only indirectly—and this is reflected in data. Data always speaks. It always has a story to tell, and there’s always something to learn from it. Data scientists see this over and over again across predictive analytics projects. Pull some data together and, although you can never be certain what you’ll find, you can be sure you’ll discover valuable connections by decoding the language it speaks and listening.”

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Ken Cukier on the How Big Data will Change the World

 

In this video from The Next Web Conference Europe 2013, Ken Cukier, Data Editor at the Economist describes how Big Data hype should not deter us from bringing this phenomenon to its full potential to change the world.


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Video: EIOW Exascale I/O Working Group

 

In this video from the Lustre User Group 2013, Meghan McClelland from Xyratex presents: EIOW – Exascale I/O Working Group.

There is a fierce competition on the storage market to offer the best performing devices, with great management at a low price. The EIOW group, from the outset, decided that it would not attempt to offer an end-to-end solution, which would necessarily involve competing instead of working with storage providers. The focus of EIOW is on middleware to provide, for example, schemas describing data structure and layout, novel access methods to data for applications, a uniform data management infrastructure and a framework for the implementation of layered I/O software, similar in spirit to HDF5 as a specialized use of a parallel file system. We decided EIOW should be open, and have interfaces to layer on lower level storage infrastructure such as object stores, databases and file systems as provided by storage providers, to allow their expertise and leadership in this area to continue to benefit the HPC community.

Download the EIOW whitepaper and slides, or check out our LUG 2013 Video Gallery.


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Is All Science Becoming Data Science?

 

Over at Science Magazine, Vijaysree Venkatraman writes that data-driven discovery may soon become the norm in science and that learning to code and becoming comfortable with large datasets may soon be a necessity in many traditional scientific fields.

All science is fast becoming what is called data science,” says Bill Howe of UW’s eScience Institute. Today, there are sensors in gene sequencers, telescopes, forest canopies, roads, bridges, buildings, and point-of-sale terminals. Every ant in a colony can be tagged. The challenge is to extract knowledge from this vast quantity of data and transform it into something of value. Lately, Lazowska says, he has been hearing this refrain from researchers in engineering, the sciences, the social sciences, law, medicine, and even the humanities: “I am drowning in data and need help analyzing and managing it.”

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Video: Why the Size of the Data Does Not Define Big Data

 

In this video from the 2013 HPC User Forum, John Hengeveld from Intel presents: Big Data Use Cases – The Size of the Data does not define Big Data.

Download the slides (PDF) or check out the HPC User Forum Video Gallery.


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Beyond Batch – Moving Hadoop to Multi App with Apache YARN

 

Over at the Hortonworks Blog, Arun Murthy writes that Apache Hadoop NextGen MapReduce (YARN) provides the highly-sought-after ability to run SQL in Hadoop.

When we set out to build Hadoop 2.0, we wanted to fundamentally re-architect Hadoop to be able to run multiple applications against relevant data sets. And do so in a way where multiple types of applications can operate efficiently and predictably within the same cluster – this is really the reason behind Apache YARN, which is foundational to Hadoop 2.0. By managing the resource requests across a cluster, YARN turns Hadoop from a single application system to a multi-application operating system.

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Video: Today’s I/O Challenges for Big Data Analysis

 

In this video from the 2013 HPC User Forum, Henry Newman from Instrumental presents: Today’s I/O Challenges for Big Data Analysis.

Those who own the archive own the big data solutions as you cannot move data around.

Download the slides (PDF) or check out the HPC User Forum Video Gallery.


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Video: High-Performance Data Analysis: HPC Meets Big Data

 

In this video from the 2013 HPC User Forum, Steve Conway and Chirag Dekate from IDC present: High-Performance Data Analysis: HPC Meets Big Data.

Download the slides (PDF) or check out our HPC User Forum Video Gallery.


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Splunk and Cloudera Alliance Hints at New Big Data Landscape

 

Over at SlashBI, Nick Kolakowski writes that the newly announced Splunk/Cloudera alliance hints at the new Big Data lanscape.

Under the terms of the alliance, Splunk Hadoop Connect will link Splunk Enterprise to Cloudera Enterprise, Cloudera’s Hadoop distribution (and associated projects). Which is certainly good for Cloudera: any number of companies have released Hadoop distributions over the past couple months, crowding the marketplace, and making it all the more vital for individual firms to sign “alliances” and other contracts for their respective offerings. Such alliances could also prove vital for smaller firms seeking to hold the line, as it were, against IT giants such as IBM and SAP. The latter, of course, have untold millions of dollars and large amounts of other resources to deploy in the search for data-analytics customers; faced with that sort of competition, startups and midsize companies need to consider how partnerships can amplify the reach of their analytics products.

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