Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges

Big Data Analysis: Big Data Analysis Pipeline and Its Technical Challenges

Rajanala Vijaya Prakash (S. R. Engineering College, India)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/978-1-5225-0182-4.ch006
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The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.
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2. Background

Practically everything on the Internet is recorded. When you search on Google or Bing, your queries and subsequent clicks are recorded. When you shop on Amazon or eBay, not only every purchase, but every click is captured and logged. When you read a newspaper online, watch videos, or track your personal finances, your behaviour is recorded. The recording of individual behaviour does not stop with the Internet: text messaging, cell phones and geo locations, scanner data, employment records, and electronic health records are all part of the data.

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