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Open Access Knowledge and Scholarship

Date of Award


Culminating Project Type

Starred Paper

Degree Name

Computer Science: M.S.


Computer Science and Information Technology


School of Science and Engineering

First Advisor

Donald Hamnes

Second Advisor

Dennis Guster

Third Advisor

Ramnath Sarnath

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Big Data, Hive, Hadoop, Netezza, Data Warehouse


Considering the challenges posed by Big Data, the cost to scale traditional data warehouses is high and the performances would be inadequate to meet the growing needs of the volume, variety and velocity of data. The Hadoop ecosystem answers both of the shortcomings. Hadoop has the ability to store and analyze large data sets in parallel on a distributed environment but cannot replace the existing data warehouses and RDBMS systems due to its own limitations explained in this paper. In this paper, I identify the reasons why many enterprises fail and struggle to adapt to Big Data technologies. A brief outline of two different technologies to handle Big Data will be presented in this paper: Using IBM’s Pure Data system for analytics (Netezza) usually used in reporting, and Hadoop with Hive which is used in analytics. Also, this paper covers the Enterprise architecture consisting of Hadoop that successful companies are adapting to analyze, filter, process, and store the data running along a massively parallel processing data warehouse. Despite, having the technology to support and process Big Data, industries are still struggling to meet their goals due to the lack of skilled personnel to study and analyze the data, in short data scientists and data statisticians.



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