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Data Analytics with Python for Data Scientists

Data Science with Hadoop for Statistics and Text Analysis

From Wikipedia

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing datadata storagedata analysis, search, sharingtransfervisualizationquerying, updating and information privacy. There are three dimensions to big data known as Volume, Variety and Velocity.

Lately, the term “big data” tends to refer to the use of predictive analyticsuser behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”[2] Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.”[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searchfintechurban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorologygenomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]

Data sets grow rapidly – in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] By 2025, IDC predicts there will be 163 zettabytes of data.[10] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[11]

Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”.[12] What counts as “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”[13]

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