logoDigital Platform Architect

Observability & Operations

Digital platform observability and monitoring with comprehensive telemetry, distributed tracing, and multi-layer visibility for reliable operations.

Observability & Operations Architecture

Detailed view showing components, connections, and data flow

SourcesNetworkTCP L4 • eBPFApplicationAPIs • GatewayServicesMicroservicesRuntimeK8s • JVM • DBCollectioneBPF AgentsOTel SDKOTel CollectorDataDog AgentSignalsMetricsTracesLogsProfilesEventsBackendsDataDog SuiteELK StackPrometheusJaeger/TempoCorrelationEngineService • Env • VersionOutputsDashboardsAlertsSLOsIncidentsRunbooksMTTD • MTTR • PII Scrubbing • Intelligent SamplingCross-Layer Correlation • Chaos Testing • Error Budgets
Core Components
Supporting Services
Data Flow
Security Boundary

Enables Architectural Patterns

Microservice Architecture

Independent, deployable services aligned to business domains, communicating via APIs, events, or streams.

Event-Driven Architecture

Systems communicate through events, enabling loose coupling, async workflows, and reactive behavior.

Serverless

Event-driven functions and managed services with autoscaling and pay-per-use.

Layered Architecture

Organize code into layers (presentation, business, persistence, database) with clear responsibilities and boundaries (often deployed across N‑tiers).

Hexagonal Architecture

Domain-centric design separating core logic from external concerns via ports (interfaces) and adapters.

What it is

Comprehensive observability platform providing end-to-end visibility across network, application, service, and infrastructure layers. Combines signals collection, intelligent correlation, and operational guardrails to ensure reliable platform operations.

Observability Signals

  • Metrics: Time-series data for KPIs, performance, and resource utilization
  • Logs: Structured application and system events with correlation
  • Traces: Distributed request flow across microservices
  • Profiles: Application performance profiling and resource analysis
  • Events: Business and system events for correlation and alerting

Data Collection Architecture

  • eBPF agents on nodes for network-level TCP Layer 4 observability
  • OpenTelemetry (OTel) SDK/auto-instrumentation in applications
  • OTel Collector or DataDog Agent on each node/sidecar
  • Custom collectors for legacy systems and specialized protocols
  • Synthetic monitoring and real user monitoring (RUM)

Backend & Analytics

  • DataDog Metrics, APM, RUM, Synthetics, Security monitoring
  • ELK Stack for log aggregation and search
  • Prometheus/Grafana for metrics and alerting
  • Jaeger/Tempo for distributed tracing storage
  • Time-series databases with retention policies

Network-Level Observability

  • TCP Layer 4 monitoring using eBPF technology
  • Network traffic analysis across Identity Server, Gateway, and microservices
  • Connection tracking, bandwidth utilization, and latency monitoring
  • Network security monitoring and anomaly detection
  • CNI health monitoring and kube-proxy error tracking

Application-Level Observability

  • API statistics: error rates, HTTP response codes, latency
  • Gateway and Identity Provider metrics publishing
  • Application performance monitoring (APM)
  • Business metrics and user journey tracking
  • Code-level insights and performance bottleneck identification

Service-Level Observability

  • Distributed tracing: Gateway → Identity Server → Microservice → DB
  • W3C traceparent/tracestate propagation with B3 fallback
  • Span attributes: api.name, api.operation, tenant, auth.method, user.flow
  • Tail-based and dynamic sampling for performance optimization
  • Trace-log correlation with trace_id/span_id injection

Runtime & Infrastructure Observability

  • Kubernetes: Node pressure, pod restarts, OOMKilled, HPA/VPA behavior
  • JVM monitoring: Heap usage, GC pause time, thread pools, JIT stats
  • Database connections, slow queries, and performance metrics
  • Cache hit/miss ratios, evictions, and memory utilization
  • Message queue lag, requeue rates, and broker health

Correlation & Context

  • Service, environment, version, region, and tenant correlation
  • API name, user flow, and release tag tracking
  • Cross-layer correlation between network, application, and infrastructure
  • Business context integration with technical metrics
  • Anomaly detection with contextual analysis

Operational Guardrails

  • PII scrubbing and data privacy protection
  • Intelligent sampling strategies and cost optimization
  • Multi-tier data retention policies
  • SLOs and error budget management
  • Alert fatigue reduction and noise filtering

Architecture patterns

  • RED metrics (Rate, Errors, Duration) and USE metrics (Utilization, Saturation, Errors)
  • Centralized vs. federated observability models
  • Event-driven alerting and correlation engines
  • Chaos engineering and resilience testing
  • Service mesh observability with sidecar telemetry

Tech examples

  • DataDog (APM, Infrastructure, Logs, RUM, Synthetics)
  • OpenTelemetry ecosystem (Collectors, SDKs, Auto-instrumentation)
  • eBPF tools (Cilium, Falco, Pixie)
  • ELK Stack (Elasticsearch, Logstash, Kibana)
  • Prometheus, Grafana, Tempo, Loki
  • Jaeger, Zipkin for distributed tracing

KPIs/SLIs

  • MTTD (Mean Time to Detection) and MTTR (Mean Time to Recovery)
  • SLO compliance and error budget consumption
  • Alert precision: signal-to-noise ratio
  • Observability coverage: trace, log, and metric completeness
  • Platform reliability: uptime, availability, and performance SLAs