Many Faces of Real-time Analytics: A Guide to System Classification and Implementation

Overview

Explore real-time analytics systems classification, characteristics, and technology choices for implementing different analytical solutions in production environments.

Syllabus

• Introduction to Real-time Analytics
Definition and Importance
Use Cases in Various Industries
Key Challenges and Opportunities
• Classification of Real-time Analytics Systems
Batch vs. Real-time vs. Near Real-time Processing
Streaming Analytics vs. Event-Driven Architectures
Data Sources and Data Types
• Characteristics of Real-time Analytics Systems
Latency and Scalability Needs
Data Processing Models
Accuracy vs. Timeliness
• Technology Choices for Real-time Analytics
Overview of Popular Tools and Frameworks
Criteria for Selecting Technologies
Case Studies and Industry Examples
• System Architecture Design
Critical Infrastructure Components
Data Ingest and Storage Solutions
Data Processing and Pipeline Designing
• Implementation Strategies
Best Practices for Deployment
Monitoring and Maintenance
Handling Failures and Anomalies
• Real-time Machine Learning Integration
Online Learning Models
Model Deployment and Update Strategies
A/B Testing and Evaluation
• Security and Privacy Considerations
Data Governance and Compliance
Real-time Security Threats
Anonymization and Data Protection
• Hands-on Project
Designing a Real-time Analytics System
Implementing a Stream Processing Workflow
Presenting and Evaluating Solutions
• Course Summary and Future Trends
Recap of Key Concepts
Emerging Technologies in Real-time Analytics
Future Directions and Professional Development Paths
• Additional Resources and Further Reading
Recommended Books and Articles
Online Courses and Workshops
Communities and Forums for Ongoing Learning

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Subjects

Data Science