Microservices and Cloud-Native architectures have been goals of many organizations to help increase speed and agility, but as complexity grows, systems become increasingly challenging to observe. When issues occur, these issues are often difficult to triage and identify the root causes.

This course introduces a range of practices for advancing resilience and how to architect end-to-end Observability for Cloud-Native applications. The advantages of building full-stack metrics, events, logs, and distributed tracing are introduced, along with the impact of DevSecOps on Observability and how AIOPs enhance Observability capabilities. This course also covers how Network and Security Observability plays a key role in building reliability, the key aspects of security operations and automated responses are covered,

Gain tangible takeaways to leverage situations such as implementing MELT models effectively, that fit their organizational context, building distributed tracing and resiliency by design. The course is developed by leveraging key experts in the fields of telemetry, sources of knowledge and engaging with thought-leaders in the Observability space, and working with organizations who have crossed the chasm of modern Observability to extract real-life best practices.

This course positions learners to successfully complete the Observability Foundation certification exam

• What is Observability?
• MELT
• Importance of Observability
• Why Traditional Monitoring is not Enough
• Observability Maturity Model
• Challenges with Observability

• Defining telemetry
• The Three Pillars of Observability – Logs, metrics, and traces
• Distributed Traces
• Parts of a Trace
• Tracing: Error Diagnosis

• What is Observability made of?
• OpenTelemetry
• OpenTelemetry Libraries
• OpenTelemetry Agents & Collection
• The Rest of the Open Source Ecosystem

• Service maps
• Topology
• Time Travel Topology
• Escalation Graphs
• The 4 Ts

• Observability and the Data Paradox
• Why Observability need DataOps
• Data Ownership and Governance
• Data Privacy & Observability
• Data Confidentiality, Integrity & Availability
• Maintaining CIA Triad

• What is AIOps
• AIOps Platforms
• Enterprise Platform for AIOps
• AI/ML Use Cases
• Auto-Instrumenting Optimization
• Feedforward CI/CD into AIOPs
• Feedback AIOps into Quality Gates

• Observing Security
• Monitoring Security with eBPF
• Container Security
• Network Observability
• Visibility and Integration of various sources

• Observability Indicators
• Dashboards and Visualization
• Chaos Engineering

On completion of this course, the following learning outcomes will be achieved:

• Implement an Observability culture in your organization
• Know the principles of Observability and differentiate with monitoring in modern architectures
• Understanding the three pillars of Observability
• Adopting open Telemetry standards
• Understand Observability Maturity Models
• Implementing full stack Observability and distributed tracing
• Observability using AI for incident management.
• Implementing Network, Container level Observability with security as a first-class citizen
• Time-based Topology for Observability
• The Data paradox, and addressing data issues using a systematic approach
• Observability practices for DevSecOps and SRE

The target audience for this course are professionals, such as:

• Anyone focused on large-scale service scalability and reliability
• Anyone interested in modern IT leadership and organizational change approaches
• Business Managers and Stakeholders
• Change Agents
• Consultants
• DevOps Practitioners
• IT Directors, Managers, Team Leaders
• Product Owners, Scrum Masters
• Software Engineers
• Site Reliability Engineers
• System Integrators
• Tool Providers

• 40 Multiple Choice
• 1 mark per correct answer
• 26 marks required to pass  (out of 40 available) – 65%
• Sixty minutes duration
• Web-based Open-book exam

There are no pre-course reading resources or assignments prior to attending the course.