November, 2014

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Emerging Technology Resumes: How to make a lasting impact

ProjectPro

A good hadoop big data resume might not be enough to get you selected but a bad hadoop big data resume is enough for rejection.Many big data professionals consider writing big data hadoop resume as an exercise in psychological warfare. Are you one among them? Do you want to move your big data hadoop resume from the slush pile to the "YES" pile ,then you must follow some important guidelines to ensure that your hadoop big data resume does not land into the "NO" pile of CV's.This article aims to p

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Hadoop MapReduce vs. Apache Spark Who Wins the Battle?

ProjectPro

Confused over which framework to choose for big data processing - Hadoop MapReduce vs. Apache Spark. This blog helps you understand the critical differences between two popular big data frameworks. Hadoop and Spark are popular apache projects in the big data ecosystem. Apache Spark is an improvement on the original Hadoop MapReduce component of the Hadoop big data ecosystem.

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MongoDB and Hadoop

ProjectPro

Hadoop is the way to go for organizations that do not want to add load to their primary storage system and want to write distributed jobs that perform well. MongoDB NoSQL database is used in the big data stack for storing and retrieving one item at a time from large datasets whereas Hadoop is used for processing these large data sets. For organizations to keep the load off MongoDB in the production database, data processing is offloaded to Apache Hadoop.

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Hadoop 2.0 (YARN) Framework - The Gateway to Easier Programming for Hadoop Users

ProjectPro

With a rapid pace in evolution of Big Data, its processing frameworks also seem to be evolving in a full swing mode. Hadoop (Hadoop 1.0) has progressed from a more restricted processing model of batch oriented MapReduce jobs to developing specialized and interactive processing models (Hadoop 2.0). With the advent of Hadoop 2.0, it is possible for organizations to create data crunching methodologies within Hadoop which were not possible with Hadoop 1.0 architectural limitations.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.