Hadoop Training

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Learn the basics of Hadoop. You will learn about the Hadoop architecture, HDFS, MapReduce, Pig, Hive, JAQL, Flume, and many other related Hadoop technologies. Practice with hands-on labs on a Hadoop cluster using any of these methods: On the Cloud, with the supplied VMWare image, or install locally.

Course Syllabus 

Introduction to Hadoop

Hadoop architecture

Introduction to MapReduce

Querying data

Hadoop administration

Moving data into Hadoop

General Information 1. This course is free. 2. It is self-paced. 3. It can be taken at any time. 4. It can be taken as many times as you wish. 5. Labs can be performed on the Cloud, or using a 64-bit system. If using a 64bit system, you can install the required software (Linux-only), or use the supplied VMWare image. More details are provided in the section "Labs setup". 6. Students passing the course (by passing the final exam) will have immediate access to printing their online certificate of achievement. Your name in the certificate will appear exactly as entered in your profile in BigDataUniversity.com. 7. If you did not pass the course, you can take it again at any time.



Recommended skills prior to taking this course 

Basic knowledge of operating systems (UNIX/Linux)

Grading Scheme 

The minimum passing mark for the course is 60%, where the final test is worth 100% of the course mark.

You have 3 attempts to take the test.

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.

Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[2] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[3] connectomics, complex physics simulations, biology and environmental research.[4]

Data sets are growing rapidly in part because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[5][6] The world's technological percapita capacity to store information has roughly doubled every 40 months since the 1980s;[7] as of 2012, every day 2.5 exabytes (2.5×1018) of data are created.[8] One question for large enterprises is determining who should own big data initiatives that affect the entire organization.[9]

Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[10] What is considered "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."[11] Definition[edit] Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.[12] Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale. [13]

In a 2001 research report[14] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.[15] In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Gartner's definition of the 3Vs is still widely used, and in agreement with a consensual definition that states that "Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value".[16] Additionally, a new V "Veracity" is added by some organizations to describe it,[17] revisionism challenged by some industry authorities.[18] The 3Vs have been expanded to other complementary characteristics of big data:[19][20]

Volume: big data doesn't sample; it just observes and tracks what happens Velocity: big data is often available in real-time Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion Machine Learning: big data often doesn't ask why and simply detects patterns[21] Digital footprint: big data is often a cost-free byproduct of digital interaction[20] The growing maturity of the concept more starkly delineates the difference between big data and Business Intelligence:[22]

Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends, etc..

Big data uses inductive statistics and concepts from nonlinear system identification[23] to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density[24] to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors.[23][25] In a popular tutorial article published in IEEE Access Journal,[26] the authors classified existing definitions of big data into three categories: Attribute Definition, Comparative Definition and Architectural Definition. The authors also presented a big-data technology map that illustrates its key technological evolutions.

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