Specialization in observability and AIOps with Dynatrace

Specialization Course in Observability and AIOps with Dynatrace

The Observability and AIOps Specialization is designed for professionals seeking to lead observability, automation, and intelligent operations strategies. It covers signal correlation, advanced Kubernetes, artificial intelligence, automation, and AppSec.

This syllabus is designed at an expert level, above Associate and Professional, and covers everything an advanced consultant needs to operate complex environments, automate operations with AI, and enable end-to-end observability in modern organizations.

Virtual course / teletraining - 185 hours - 100% creditable

Dynatrace Observability Course - AIOps - Intelligent Monitoring - Digital Performance Optimization - Real-time Analytics - IT Automation - Observability Training - Proactive Systems Management

Training objectives

• Design end-to-end observability strategies.
• Correlate metrics, logs, and traces using advanced techniques.
• Use Davis AI for prediction and root cause analysis.
• Implement advanced observability in Kubernetes.
• Integrate observability into CI/CD pipelines (Quality Gates).
• Automate operations with AutomationEngine.
• Detect vulnerabilities using AppSec + observability.
• Apply AIOps in enterprise environments.

Addressed to

• SREs (Site Reliability Engineers) who seek to automate operations with AI.
  
• Senior DevOps engineers who handle Kubernetes, multi-cloud, and complex microservices.
  
• Cloud architects and enterprise architects who must define global observability models.
  
• Engineering teams of large organizations that require observability at scale.
  
• Professionals who lead the observability, resilience, and performance strategy in the company.
  
• Expert consultants in systems optimization, AIOps, AppSec and automation.

It is the expert level, ideal for those who wish to implement corporate observability, intelligent automation (AIOps), CI/CD quality and end-to-end strategies.

Course Content: Specialization in Observability and AIOps with Dynatrace

1. Advanced Foundations of Modern Observability

• What is real observability (beyond monitoring)?

• “3-signal” vs “5-signal” model:

  • Metrics

  • Logs

  • Traces

  • Topologies/entities

  • User experience

• How Dynatrace fits into the modern observability stack.

• Observability in distributed systems, containers, and microservices.

• Current standards: OpenTelemetry, eBPF, W3C Trace Context.

2. Full-Stack Depth Observability

Infrastructure

• Physical and virtual hosts.

• Hybrid cloud: AWS, Azure, GCP.

• Cloud managed resources (RDS, App Service, Functions, Lambda…).

Applications

• Distributed microservices.

• Languages ​​and deep instrumentation.

• Identification of services, dependencies and flows.

• Advanced PurePaths in cloud-native environments.

Containers and Kubernetes

• Native Kubernetes observability architectures.

• Monitoring at the level of:

  • Nodes

  • Pods

  • Namespaces

  • Ingress

  • Control plane

• Integration with sidecars, service mesh (Istio/Linked).

• Advanced auto-discovery by Dynatrace OneAgent and eBPF.

3. Observability Data: Normalization, Enrichment, and Correlation

• How Dynatrace automatically correlates metrics, logs, and traces.

• Advanced log enrichment (parsing, pipelines, metadata).

• Data Explorer in advanced mode:

  • Dimensions, aggregations, dynamic filters.

  • DQL (Dynatrace Query Language), if applicable (SaaS/future).

• Notebooks for in-depth analysis and interactive documentation.

• Prioritization based on business impact and SLOs.

4. AIOps with Dynatrace (Davis AI)

Explainable AI Engine

• How Davis automatically identifies problems.

• Detection algorithms: causal, topological, contextual, behavioral.

• Real noise reduction.

• Anomaly detection based on behavior, not thresholds.

Advanced Root Cause Analysis

• In-depth root cause analysis in distributed environments.

• Multiple causes and non-evident dependencies.

• Identifying bottlenecks in microservices/k8s.

• "Blast radius" analysis to understand impact.

Prediction and prevention

• Predictive KPIs.

• Behavioral models.

• Early detection of degradation.

5. AIOps + CI/CD: Integration with pipelines

• Automated quality tests with observability.

• “Quality Gates” based on SLOs and metrics.

• Integration with pipelines:

  • Azure DevOps

  • GitHub Actions

  • Jenkins

  • GitLab

• Automations for rollback, approval gates, and continuous performance analysis.

6. Advanced automation with Dynatrace AutomationEngine

• Introduction to the automation engine.

• Intelligent workflows with AI.

• Automation of operational tasks:

  • Restart of services

  • Automatic scaling

  • Incident Management

• Integrations with:

  • PagerDuty

  • ServiceNow

  • Slack / Teams

  • Webhooks

• Observability as a platform for automating decisions.

7. Security and Observability (SecOps + AppSec)

• Complete AppSec view + observability.

• Real-time vulnerability detection:

  • Log4Shell, Spring4Shell, etc.

• Scanning of libraries and dependencies.

• Execution protections (RASP-like).

• Prioritizing vulnerabilities with AI.

• Integration with corporate SIEM/SOAR.

8. Enterprise Observability Strategies

• How to design a company-level observability strategy.

• Guidelines for integrating DevOps, SRE, SecOps and Cloud teams.

• Data and equipment governance.

• Advanced access and permissions management.

• Multi-cluster, multi-cloud, and multi-environment observability.

• Migration towards a DataOps + ObservabilityOps model.

9. Advanced use cases (enterprise & cloud-native)

• High-traffic e-commerce.

• Multicloud platforms.

• Banking/finance microservices.

• Edge computing and IoT.

• Critical and high availability systems.

• Observability of enterprise APIs.

• Real-time performance in streaming or gaming.

10. Practical laboratories

• Deployment of advanced observability in a Kubernetes cluster.

• Log pipeline configuration and enrichment.

• Creation of multi-dimensional dashboards.

• RCA analysis of a simulated serious problem.

• Creating an automated workflow with AI-based actions.

• Dynatrace integration with a CI/CD pipeline and Quality Gates.

• Vulnerability detection and prioritization.

Expected results

Upon completion of this specialization, the student will be able to:

• Design complete observability and AIOps architectures.

• Implement advanced observability in Kubernetes and microservices.

• Automatically correlate metrics, logs, traces, and topologies.

• Use Davis AI for root cause analysis and prediction.

• Automate operations with intelligent workflows.

• Integrate observability into DevOps pipelines and continuous QA.

• Operate as a senior level SRE/Observability Engineer/AIOps Consultant .

*Nanfor is not an official Dynatrace center

Prerequisites

• Have completed Dynatrace Professional or equivalent.
• Experience in Kubernetes and microservice architectures.
• Solid knowledge of cloud computing.
• Familiarity with scripting/automation.
• Knowledge of DevOps pipelines.

Language

• Spanish / English