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Big Data Divide: The challenge of a modern-day data and analytics pla…

Note: The corrected, updated and expanded version of this article is found here: http://www.bit.ly/didata002

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The young tech & social media companies inspired us all -- and even donated their ground breaking tools -- to do big data analytics. But it's not easy derive the same benefit from that when you're not 'Google'. http://www.bit.ly/didata002

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9 Hadoop Companies You Should Know

9 Hadoop Companies You Should Know | BigData | Scoop.it

If you've got a lot of data, then Hadoop either is, or should be on your radar. (9 Hadoop Companies You Should Know - http://t.co/New2tvXz0q)

prismalytics's insight:

In contrast to the direction of this article, DIData recommends that organizations take a  modern-day approach to adopting data analytics and NoSQL platforms, by engaging a hybrid of tech & business-savvy personnel and boutique big-data consultancies that place a first order emphasis on understanding it's analytic use-cases, and then on selecting technology stacks to enable them. This yields more agile results while simultaneously aligning organizations to the social media companies, open source communities, and university research departments that collectively gave birth to the modern-day, barrier-crushing, data analytics ecosystem.

 

The 'old school' Tier-1 road-map centric approach will under-deliver since agility will suffer under the weight of costly, underachieving, and proprietary solutions that don't account for current and future business use-cases.

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Hadoop Yarn Tutorial: Map-Reduce Job Execution on YARN

prismalytics's insight:

The YARN-era of Hadoop isn't just a technical evolution: clusters built with YARN (version-2 Hadoop) increase the ROI of capital purchases since -- unlike Map/Reduce centric version-1 of Hadoop -- the YARN framework allows you to run multiple distributed compute engines on top of it: Map/Reduce, Storm (by twitter), and so on. Where use-case tolerances allow, DiData encourages a consideration towards building analytics solutions on top of YARN or MESOS. They keep the 'commodity' in your commodity-based clusters.

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Spark Summit 2013 - Spark Query Service (Job Server) at Ooyala - Evan Chan, Kelvin Chu

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