Hadoop and MapReduce: Big Data Analytics

G00208798

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Summary

Big data analytics and the Apache Hadoop open source project are rapidly emerging as the preferred solution to address business and technology trends that are disrupting traditional data management and processing. Enterprises can gain a competitive advantage by being early adopters of big data analytics.

Table of Contents

  • Summary of Findings
  • Analysis
    • Disruptive Changes on Traditional Data Processing
      • Increasing Data Volumes
      • Increasing Data Complexity
      • Increasing Analysis Complexity
      • Changing Analysis Model
      • Increased Acceptance of Open Source Software
      • Increasing Availability of Cost-Effective Compute and Storage
    • Data-Processing Pipeline
    • Structured Data
    • Big Data Analytics Classification
      • Speed of Decision Making
      • Processing Complexity
      • Transactional Data Volumes
      • Data Structure
      • Flexibility of Processing/Analysis
      • Throughput
      • Summary
    • Big Data Analysis Technology
      • Data Management Frameworks
      • Processing Frameworks
      • Development Frameworks
      • Modeling Frameworks
      • Management Frameworks
      • Integration Frameworks
      • Open Source Hadoop
      • Hadoop in the Cloud
      • Hadoop Hardware
      • Hadoop Futures
      • Hadoop Use Cases
    • Big Data Analysis as a Competitive Advantage
    • Strengths
    • Weaknesses
  • Recommendations
    • Adopt Big Data Analytics and the Hadoop Open Source Project to Meet the Challenges of the Changing Business and Technology Landscape
    • Adopt a Packaged Hadoop Distribution to Reduce Technical Risk and Increase the Speed of Implementation
    • Be Selective About Which Hadoop Projects to Implement
    • Use Hadoop in the Cloud for Proof of Concept
    • The Big Data Analytics Initiative Should Be a Joint Project Involving Both IT and Business
    • Enterprises Should Not Delay Implementation Just Because of the Technical Nature of Big Data Analytics
    • Adapt Existing Architectural Principles to the New Technology
  • Recommended Reading
© 2011 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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