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Visualizing Streaming Data

Book Description

While tools for analyzing streaming and real-time data are gaining adoption, the ability to visualize these data types has yet to catch up. Dashboards are good at conveying daily or weekly data trends at a glance, though capturing snapshots when data is transforming from moment to moment is more difficult—but not impossible.

With this practical guide, application designers, data scientists, and system administrators will explore ways to create visualizations that bring context and a sense of time to streaming text data. Author Anthony Aragues guides you through the concepts and tools you need to build visualizations for analyzing data as it arrives.

  • Determine your company’s goals for visualizing streaming data
  • Identify key data sources and learn how to stream them
  • Learn practical methods for processing streaming data
  • Build a client application for interacting with events, logs, and records
  • Explore common components for visualizing streaming data
  • Consider analysis concepts for developing your visualization
  • Define the dashboard’s layout, flow direction, and component movement
  • Improve visualization quality and productivity through collaboration
  • Explore use cases including security, IoT devices, and application data

Table of Contents

  1. Preface
    1. Who This Book Will Benefit
    2. How This Book Is Organized
    3. Conventions Used in This Book
    4. Using Code Examples
    5. O’Reilly Safari
    6. How to Contact Us
    7. Acknowledgments
  2. 1. Introduction
    1. Why Visualizations
    2. The Standard
    3. Terms
    4. Data Formats
    5. Data Visualization Applications
    6. Assumptions and Setup
  3. 2. Goals
    1. Presentation Goals
    2. Pre-batch Analysis
    3. The Analyst Decision Queue
    4. Data Pipeline Visualization
    5. Show Movement on a Map
    6. Asking New Questions
    7. Seeing Frequency and Order
  4. 3. Data Sources
    1. Data Source Types
    2. What to Stream
    3. Data Storage Considerations
    4. Managing Multiple Sources
  5. 4. Streaming Your Data
    1. How to Stream Data
    2. Buffering
    3. Streaming Best Practices
  6. 5. Processing Streaming Data for Visualization
    1. Batch Processing
    2. Inline Processing
    3. Processing Patterns
    4. Lookups
      1. Lookup Types
    5. Normalizing Events
    6. Extracting Value
    7. The JSON Collection Decorator
    8. Processing Checklist
    9. Streaming Statistics
      1. Types of Statistics
    10. Record Context Checklist
    11. Scaling Data Streams
    12. Presenting Processing
  7. 6. Developing a Client
    1. Native or Browser Development
    2. Frameworks and Libraries
    3. A Common Approach
    4. Getting Started with the Sample Client Application
    5. Client Libraries
    6. Code Structure
    7. Alternative Approaches
  8. 7. Presenting Streaming Data
    1. Showing Streaming Data
      1. Events
      2. Logs
      3. Records
    2. Dashboards
    3. Visual Elements and Properties
    4. Data Density
    5. Dividing Time
    6. Time to Live
    7. Context
    8. Visual Language
    9. Appropriate Displays
  9. 8. Visualization Components
    1. Records
    2. Statistics
    3. Visualizations
    4. Streaming Options for Common Visualizations
    5. Streaming Visualization Techniques
    6. Bar Chart Example
    7. Static Information
  10. 9. Streaming Analysis
    1. Visual Distractions
    2. Visual Deception
    3. Cognitive Bias
    4. Analysis Models
    5. Visual Analysis
    6. Streaming Analysis Workflow
    7. Context Awareness
    8. Outliers Example
  11. 10. Workflow Visualization
    1. Updating Processing
    2. Interacting with Visualizations
    3. Storing Decisions
  12. 11. Streaming Data Dashboard
    1. Layout
    2. Flow Direction
    3. Component Movement
    4. Autopilot
  13. 12. Machine Learning
    1. Machine Learning Primer
    2. Machine Learning and Streaming Data Visualization
    3. Presenting Machine Learning Results
    4. Supervised Learning and Continuous Tuning
    5. Presenting the Unexpected
    6. Machine Learning Decisions on What to Display
  14. 13. Collaboration
    1. Why Collaborate
    2. Sharing Out
  15. 14. Exports
    1. Configurations
    2. Datasets
    3. Streaming Replay Reports
    4. Static Reports
    5. Submitting Processing Updates for the Data Feed
  16. 15. Use Cases
    1. Security
    2. Machine Learning Interaction
    3. Smart Devices (aka the Internet of Things)
    4. Brand Monitoring
    5. Public Opinion
    6. Application Data
    7. Error Monitoring
    8. Collaboration
    9. Workflow
    10. Analyst Input
    11. Data Exploration
    12. Examples
      1. Powerboard
      2. Vizceral
      3. Alooma Live
      4. Stream-Viz
  17. 16. Summary and References
    1. Links Mentioned
    2. Data
    3. Transform and Filter
    4. Presentation Dashboards and Components
    5. Interactions and Actions
    6. Beyond the System
  18. Index