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A survey on platforms for big data analytics.

Dilpreet Singh1, Chandan K Reddy1

  • 1Department of Computer Science, Wayne State University, Detroit, MI 48202 USA.

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|July 21, 2015
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Summary
This summary is machine-generated.

This study analyzes big data analytics platforms, evaluating hardware and software for scalability, performance, and fault tolerance. It guides informed platform selection based on computational needs and algorithm requirements.

Keywords:
Big dataMapReducebig data analyticsbig data platformsgraphics processing unitsk-means clusteringreal-time processingscalability

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Area of Science:

  • Computer Science
  • Data Science

Background:

  • Big data analytics is crucial for extracting insights from large datasets.
  • Selecting appropriate hardware and software platforms is essential for efficient big data processing.

Purpose of the Study:

  • To provide an in-depth analysis of diverse big data analytics platforms.
  • To assess the advantages and drawbacks of various hardware and software frameworks.

Main Methods:

  • Surveyed hardware platforms for big data analytics.
  • Assessed platforms based on scalability, data I/O, fault tolerance, real-time processing, data size, and iterative task support.
  • Described software frameworks, their strengths, and weaknesses.

Main Results:

  • Qualitative comparison of platforms using a star ratings table for critical characteristics.
  • Detailed pseudocode implementations of the k-means clustering algorithm on different platforms.

Conclusions:

  • The analysis aids readers in making informed decisions for platform selection based on computational needs.
  • Understanding platform characteristics is key for effective big data analytics and algorithm implementation.