Gartner Research

Stream Processing: The New Data Processing Paradigm

Summary

Digital businesses must use stream processing to meet their needs for continuous intelligence and real-time analytics. This research helps technical professionals implement stream processing architectures for data integration, event processing and analytics.

Published: 09 April 2019

ID: G00378063

Analyst(s): Sumit Pal

Table Of Contents

Analysis

  • What Is Stream Processing?
  • Batch vs. Stream Processing
  • Stream Processing Approaches
  • Stream Processing Architectural Components
  • Streaming Ingestion and Integration
  • Stream Processing Engine
  • Streaming Analytics/Streaming ML
  • Stream Processing Databases
  • Types of Streaming Systems
    • Stream Processing Engines and Frameworks
    • Stream Processing Libraries
    • Streaming Data Flow Systems
    • Stream Processing Products
  • Stream Processing Selection Criteria
  • Major Challenges in Stream Processing
  • Strengths
  • Weaknesses

Guidance

  • Which Streaming Type Should You Use?
    • Choosing Frameworks, Engines Libraries or Data Flow Systems

The Details

  • Stream Processing Concepts
    • Processing Semantics and Delivery Guarantees
    • Concept of Multiple Types of Times in a Streaming Application
    • Windowing
    • Using Watermarks for Managing Time Windows
    • Checkpointing
    • State Management
    • Back Pressure

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