Architectural Principles

Security and Governance

• Protect telemetry data in transit and at rest.
• Enforce least privilege for monitoring systems and automation engines.
• Maintain immutable, auditable event trails.

Scalability and Flexibility

• Support diverse telemetry sources across applications, infrastructure, and cloud.
• Allow multiple analytical destinations without refactoring pipelines.
• Use loosely coupled components to avoid vendor lock-in.

Extensibility

• Integrate new tools without rearchitecting foundational components.
• Permit enhancements to dashboards, analytics, and automation over time.

Operational Integrity

• Provide reliable data transport with backpressure controls.
• Support high-volume ingestion with minimal processing overhead.
• Allow both raw and enriched data flows for different use cases.

Core Architectural Tiers

1. Telemetry Sources

Telemetry originates from multiple domains, including:

• Applications
  Three-tier applications, microservices, containerized workloads.
• Shared Services
  Middleware, databases, message brokers, batch systems.
• Infrastructure
  Network, compute, virtualization, storage platforms.
• Cloud Services
  Native monitoring and log exports from AWS, Azure, and GCP.

Sources must support metrics, logs, traces, events, configuration updates, and change data.

2. Monitoring Tools (Collection Layer)

This layer collects availability, performance, capacity, and change-related telemetry. It supports both agent-based and agentless models.

Example Solutions for This Function

Enterprise

• Application performance monitoring suites
• Infrastructure monitoring platforms
• Network performance management tools

Open Source

• Prometheus
• Grafana Agent
• Zabbix
• OpenTelemetry Collector (agent mode)

Cloud-Native

• AWS CloudWatch
• Azure Monitor
• Google Cloud Operations

3. Forwarding and Enrichment Tier (Data Stream Layer)

This tier normalizes, enriches, and routes logs, metrics, and traces. It is responsible for:

• Parsing, shaping, and standardizing events
• Adding metadata (CMDB data, tags, topology)
• Filtering, sampling, and masking
• Routing to downstream analytical systems
• Supporting RAW and REPLAY paths

Example Solutions for This Function

Enterprise

• Cribl
• Confluent
• Cloudera
• Splunk

Open Source

• Fluent Bit / Fluentd
• Logstash
• Vector
• Kafka Connect

Cloud-Native

• AWS Kinesis Agent
• Azure Diagnostics Extension
• Google Cloud Logging Agent

4. Data Lake (Historical Store)

A central repository for long-term retention, analytics, and exploratory data science use cases. This tier separates low-cost archival storage from higher-cost real-time analytics engines.

Example Solutions for This Function

Enterprise

• Elastic-based storage platforms
• Enterprise data platforms for telemetry
• Snowflake (log analytics use cases)

Open Source

• OpenSearch
• ClickHouse
• Apache Druid

Cloud-Native

• Amazon S3 + Athena
• Azure Data Lake Storage
• Google BigQuery

5. Event Transport / Message Bus (Observability Pipeline)

This layer provides scalable, high-throughput event distribution.

Functions include:

• Decoupling producers from consumers
• Supporting multiple downstream consumers
• Handling backpressure
• Providing durability guarantees

Example Solutions for This Function

Enterprise

• Confluent Platform
• Enterprise-grade messaging buses

Open Source

• Apache Kafka
• RabbitMQ
• NATS
• Redpanda

Cloud-Native

• AWS Kinesis Streams
• Azure Event Hubs
• Google Pub/Sub

6. Analytics and Correlation Engines (Destinations)

Downstream systems that ingest events for analysis, correlation, detection, and visualization.

This tier includes:

• AIOps correlation
• Security analytics (SIEM)
• Application performance management
• Infrastructure analytics

Example Solutions for This Function

Example Solutions for This Function

Enterprise

• Moogsoft
• BigPanda
• IBM Watson AIOps
• ServiceNow AIOps
• Splunk ITSI
• Datadog

7. Visualization Layer (Dashboards)

Interactive dashboards for operations, engineering, leadership, and business stakeholders.
Dashboards are never part of the data path; they are consumers of analytical outputs.

Example Solutions for This Function

Enterprise

• Splunk
• Grafana
• Datadog

8. Automation and Self-Healing

This tier enables closed-loop remediation by translating insights into action.
Capabilities include:

• Automated incident response
• System configuration changes
• Infrastructure orchestration
• Policy enforcement

Example Solutions for This Function

Enterprise

• Ansible
• BigFix
• ML Toolkit

9. ITSM, CMDB, and Notification

This tier governs incident handling, change tracking, service mapping, and human notifications.

Responsibilities include:

• Incident creation
• Enrichment with CMDB data
• Escalation and notification routing
• Compliance documentation

Example Solutions for This Function

Enterprise

• xMatters
• Alert Manager
• Pager Duty

Data Flow Summary

1. Sources generate logs, metrics, traces, and events.
2. Monitoring tools collect and forward telemetry.
3. Gateways normalize and enrich the data.
4. Message bus distributes enriched and raw streams.
5. Data lakes store long-term historical data.
6. Analytics engines consume data for correlation, detection, and insights.
7. Dashboards visualize results for various roles.
8. Automation engines act on insights to remediate issues.
9. ITSM and notifications ensure human and system alignment.

Operational Considerations

• Data Quality
  Consistent metadata is essential for analytics accuracy.

• Cost Optimization
  Balance hot analytics with cold archival storage.

• Security
  Telemetry often includes sensitive information; implement strict controls.

• Retention
  Define clear policies for operational, compliance, and forensic retention cycles.

• Resiliency
  Ensure high availability at the ingestion and message bus layers.

Extensibility

The architecture is designed to support:

• Additional analytics engines
• AI-based anomaly detection
• Service topology mapping
• Distributed tracing expansion
• Infrastructure-as-code integrations

Its modular structure allows incremental adoption and replacement of components without disrupting the overall system.

Conclusion

This reference architecture provides a comprehensive, vendor-neutral foundation for building modern enterprise observability. It supports high-volume telemetry ingestion, flexible analytics, automated remediation, and service management integration. Organizations can pair this model with enterprise or open-source solutions depending on requirements, budget, and maturity, ensuring an extensible platform for long-term operational excellence.