Extending OpenTelemetry to Observability

Overview

Explore OpenTelemetry's role in observability, learn how to enhance it with automation and AI for faster problem resolution in complex environments through a real-world case study and demo.

Syllabus

• Introduction to Observability
Definition and importance of observability in modern software systems
Key components: Logs, Metrics, and Traces
• Fundamentals of OpenTelemetry
Overview of OpenTelemetry project
Architecture and components: API, SDK, Data Sources, and Exporters
• Configuration and Setup of OpenTelemetry
Installing and configuring OpenTelemetry SDKs and Agents
Integrating with popular platforms and programming languages
• Enhancing OpenTelemetry with Automation
Introduction to automation in observability
Tools and best practices for automating OpenTelemetry instrumentation
Use cases for automation in observability
• Integrating AI in Observability
Role of AI in monitoring and observability
Leveraging AI to analyze and predict performance issues from telemetry data
AI-driven anomaly detection and alerting
• Real-World Case Study
Detailed examination of a real-world implementation of OpenTelemetry
Challenges faced and solutions deployed
Outcomes and best practices derived from the case study
• Live Demonstration
End-to-end demo of setting up OpenTelemetry in a complex system
Automation and AI integration for enhanced observability
Interactive troubleshooting session
• Best Practices and Future Trends
Best practices for maintaining and scaling observability systems
Emerging trends in observability, automation, and AI
Preparing for the future of observability with OpenTelemetry
• Conclusion and Q&A
Recap of key takeaways
Open floor for participant questions

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Subjects

Conference Talks