Real-Time Event Streaming Patterns for AI-Native Applications

Overview

Explore how event streaming patterns power AI applications through real-time analytics, data enrichment, and stream processing for enhanced intelligence and reliable infrastructure.

Syllabus

• Introduction to Real-Time Event Streaming
Overview of Event Streaming and Its Importance in AI
Key Concepts: Streams, Events, and Real-Time Processing
• Fundamentals of Event Streaming Architectures
Stream Processing Frameworks and Technologies
Event Sourcing and Command Query Responsibility Segregation (CQRS)
Comparison: Batch Processing vs. Stream Processing
• Event Streaming Patterns for AI Applications
Event-Driven Microservices
Data Enrichment and Transformation Patterns
Patterns for Data Aggregation in Real-Time
• Real-Time Analytics with Event Streams
Continuous Queries and Real-Time Dashboards
Real-Time Anomaly Detection
Leveraging Machine Learning in Streaming Analytics
• Processing and Managing Data Streams
Stateful vs. Stateless Processing
Handling Late Data and Stream Joins
Ensuring Data Consistency and Reliability
• Infrastructure and Scalability Considerations
Deploying and Managing Event Stream Processing on Cloud Platforms
Scaling Stream Processing Systems Horizontally
High Availability and Fault Tolerance
• Tools and Platforms for Event Streaming
Overview of Apache Kafka, Apache Flink, and Other Key Tools
Integration with AI Frameworks and Platforms
Choosing the Right Tool for Your Use Case
• Case Studies and Applications
Real-World Examples of AI-Native Applications Utilizing Event Streaming
Best Practices and Lessons Learned from Industry Leaders
• Hands-On Project
Building an AI Application with Real-Time Event Streaming
Implementing a Simple Event Processing Pipeline
• Future Trends in Real-Time Event Streaming for AI
Emerging Technologies and Innovations
The Evolving Role of AI in Stream Processing
• Summary and Review
Key Insights from the Course
Preparing for Advanced Topics and Further Learning

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Subjects

Data Science