Harness Streaming Data for Real-Time Analytics


Archived Published: 03 November 2016 ID: G00315223

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Summary

Two architectural approaches — called Lambda and Kappa — are blueprints for achieving fast access to fresh, streaming data. Data and analytics leaders can reduce cost and accelerate implementation of real-time analytics and IoT systems by adopting these approaches.

Table of Contents

  • Introduction
  • Analysis
    • Implement Lambda Architecture to Reduce Latency of the Existing Systems
    • Implement Kappa Architecture for Building New, Fast Systems From Scratch
    • Treat Lambda and Kappa Architectures as Generic DBMS Implementation Patterns for Real-Time Analytics
      • Logging: Considerations and Tools
      • Processing Engines for Batch and Real Time: Making Trade-Offs Will Drive Your Tools Selection
      • Pay Attention to the Progress of Stream Processing Compute Platforms
      • Optimization: Considerations for Precomputed and Real-Time Views
      • Caching: Considerations and Tools for the Presentation Layer
    • Weigh Pros and Cons of Lambda and Kappa Architectures
      • Lambda Architecture
      • Kappa Architecture
    • Consider the Futures of Kappa and Lambda Architectures
  • Gartner Recommended Reading
© 2016 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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