Gartner Research

Architecture Options for Big Data Analytics on Hadoop

Published: 01 July 2015

Summary

Companies already trust Hadoop to ingest and store big data. The Hadoop ecosystem is facing the next (and expected) challenge: getting meaningful insights from data through analytics. This comparison outlines architecture patterns for implementing analytics on Hadoop.

Included In Full Research

  • Compared Architecture Patterns for Analytics on Hadoop
    • Single-Tier Architecture — In-Hadoop Analytics
    • Two-Tier Architecture — Abstracting Complexity of Hadoop
    • Three-Tier Architecture — Enabling Breadth of Analysis on Hadoop
    • Logical Data Warehouse Architecture — Comprehensive Analytic Solution
  • Assessment Criteria
  • Data Is in Hadoop, but What Is Hadoop?
  • Data Is in Hadoop, but Where Exactly?
  • Analytics Unlocks Insights in Hadoop, but How Exactly?
  • Assessment Criteria
    • User-Centric Approach
    • Analysis Criteria
    • Data Access Criteria
    • Operational Criteria
  • Bringing the Architecture Patterns for Analytics on Hadoop to Life
    • Single-Tier Architecture
    • Two-Tier Architecture
    • Three-Tier Architecture
    • LDW Architecture
  • The Architecture Patterns Apply Equally to On-Premises and Cloud Deployments
  • Expect to Implement All Four Patterns
  • Evaluate Architecture Patterns From the Audience Perspective First
  • How to Start
  • Engage Enterprise Architects and Information Architects
  • When Planning Your Architecture, Keep in Mind Future Developments and Emerging Trends
  • Sample Single-Tier Architecture
  • Sample Two-Tier Architecture in Real Life
  • Sample Three-Tier Architecture
  • The LDW

Analyst(s): Svetlana Sicular

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