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Debugging issues in microservice-based systems can be difficult because failures often cascade across services and dependencies are complex. This session explores a graph-based approach to understanding system behavior using Neo4j, combined with AI-driven reasoning. Rather than relying only on log searches and dashboards, the approach models services, dependencies, and failure signals as connected data.
The session explains why raw logs alone are often insufficient for diagnosing distributed failures and shows how operational signals can be transformed into relationships that reveal how systems behave under stress. Using graph queries, teams can examine service interactions and dependencies more clearly. By combining graph representations with AI reasoning, the approach supports faster and more explainable root cause analysis across microservice environments.
What You Will Learn
• Why raw logs alone are often insufficient for debugging complex microservice systems
• How to model services, dependencies, and operational signals as connected data using Neo4j
• How graph queries and AI reasoning can support faster and more explainable root cause analysis
Who Should Attend
• Software Developers
• Software Architects
• Platform Engineers
• Site Reliability Engineers
• DevOps Engineers
• Engineering Leads
