Gartner Research

Harness Streaming Data for Real-Time Analytics

Published: 03 November 2016

ID: G00315223

Analyst(s): Svetlana Sicular

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
  • Key Challenges

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

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