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Query Optimization for Big Data Analytics


Affiliations
1 Seidenberg School of CSIS, Pace University, White Plains, New York, United States
 

Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.

Keywords

Databases, Big data, Optimization, Analytical Query, Data Analysts and Data Scientists.
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  • Query Optimization for Big Data Analytics

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Authors

Manoj Muniswamaiah
Seidenberg School of CSIS, Pace University, White Plains, New York, United States
Tilak Agerwala
Seidenberg School of CSIS, Pace University, White Plains, New York, United States
Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains, New York, United States

Abstract


Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.

Keywords


Databases, Big data, Optimization, Analytical Query, Data Analysts and Data Scientists.

References