Enterprise Logging & Observability Framework

A standardized, reusable logging framework for Apigee shared flows, ensuring consistent telemetry, debuggability, and compliance across the API landscape.

The Problem

Inconsistent logging standards across hundreds of microservices made distributed tracing and compliance auditing nearly impossible.

Why Traditional Systems Failed

Ad-hoc logging implementations by individual teams result in fragmented data, missing correlation IDs, and potential PII leakage.

AI-Driven Approach

Architected a set of Apigee Shared Flows that abstract the complexity of logging. These flows enforce schema validation on log payloads, automatically mask sensitive fields (PII/PCI), and inject correlation contexts for distributed tracing. Integrated seamlessly with downstream log aggregators (Splunk/ELK/Dynatrace).

High-Level Architecture

[Architecture Diagram Placeholder]
Flow: Data Ingestion → Vector Embedding → LLM Router → Response Synthesis

*Detailed interaction diagrams available upon request during interview.

Business Value

Reduced mean-time-to-resolution (MTTR) for cross-service issues and ensured automated compliance with data residency policies.

Key Learnings

  • ✓Adoption is driven by ease of use; if the shared flow is hard to implement, teams will bypass it.
  • ✓Logging must be asynchronous to avoid adding latency to the critical API path.
  • ✓Standardization enables powerful downstream analytics that would be impossible with unstructured logs.