Visualizing Streaming Data. Interactive Analysis Rydułtowy

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