ID Number: G00208798



Hadoop and MapReduce: Big Data Analytics
14 January 2011
 
Marcus Collins  

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.





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Table of Contents

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
Tables
Table 1.
Structured Data Definitions
Table 2.
Cloudera's Distribution for Hadoop
Table 3.
Big Data Analytics Business Patterns
Figures
Figure 1.
Traditional Data-Processing Pipeline
Figure 2.
Emerging Data-Processing Pipeline
Figure 3.
Speed of Decision Making
Figure 4.
Processing Complexity
Figure 5.
Transactional Data Volumes
Figure 6.
Data Structure
Figure 7.
Flexibility of Processing/Analysis
Figure 8.
Throughput
Figure 9.
Summary of Big Data Characteristics
Figure 10.
Big Data Analysis Technology Stack
Figure 11.
HDFS Cluster Data Redundancy
Figure 12.
MapReduce Example
Figure 13.
MapReduce Workflow
Figure 14.
Example Hadoop Process Definition Language
Figure 15.
Hadoop as an ETL Engine
Figure 16.
Cloudera's Distribution for Hadoop
Figure 17.
BI Process




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Resource Id: 1521016