Enterprise Observability Reference Architecture

A Comprehensive Blueprint for Integrating Traditional and Cloud Environments

enterprise server room, cloud servers, IT monitoring dashboards

Highlights

Unified Observability: Leveraging Dynatrace as the core monitoring platform integrated with Datadog’s cloud expertise.
Integrated ITSM: BMC Helix provides robust incident, problem, and change management for both on-premise and cloud systems.
Cost Optimization & Open Source: Applying open source solutions like OpenTelemetry, Prometheus, and Fluentd to minimize costs while ensuring comprehensive observability.

Introduction

In today’s dynamic IT environment, an enterprise must maintain visibility across a mixture of traditional on-premise systems and modern cloud hyperscaler solutions. This reference architecture proposes an integrated approach that combines industry-leading observability tools such as Dynatrace with a substantial footprint of Datadog monitoring, along with a primary ITSM solution provided by BMC Helix.

This unified approach is designed to provide real-time insights into system performance, streamline incident management, and reduce costs by integrating open source tools for data collection, processing, and visualization. The architecture addresses challenges associated with monitoring legacy mainframes, midrange servers as well as containerized, cloud-native applications hosted on AWS, Azure, or GCP.

Architecture Overview

The reference architecture is structured into three major layers:

Data Collection Layer: Responsible for gathering telemetry data from multiple sources that include both legacy on-premise environments and modern cloud platforms.
Data Processing and Storage Layer: Processes and stores the collected data for further analysis. Utilizes both proprietary and open source data storage solutions.
Observability and ITSM Layer: Provides analysis, real-time visualization, and IT service management capabilities.

Detailed Architecture Components

1. Data Collection Layer

The data collection layer is designed to capture telemetry data from diverse sources ensuring a seamless integration of both traditional and modern systems.

1.1 Traditional On-Premise Systems

Mainframe and Midrange Servers: Traditional systems are monitored by deploying Dynatrace OneAgents or specialized collectors that interact via APIs. Where installing an agent directly is not viable, collectors are deployed to extract data via available protocols.

Legacy Applications: These systems can be integrated using open source tools such as OpenTelemetry which standardizes data collection and alleviates the risk of vendor lock-in. Additional logging data can be captured using Fluentd, a flexible open source logging aggregator.

1.2 Cloud Hyperscaler Solutions

Cloud Integration: Dynatrace monitors cloud-native applications in hyperscaler environments (AWS, Azure, GCP) with both agent-based and agentless monitoring approaches. Datadog also provides complementary monitoring capabilities, especially in environments that rely on microservices or container orchestration.

Containerized and Microservices Environments: These components are managed using Kubernetes and other orchestration environments. Native integrations provided by cloud vendors are leveraged to directly feed telemetry data into Dynatrace and Datadog.

1.3 Open Source Tools for Data Collection

OpenTelemetry: Serves as the standard for collecting metrics, logs, and traces consistently across diverse systems — both on-premise and cloud.

Prometheus and Grafana: These tools offer open-source solutions for metric collection and visualization, providing cost-effective alternatives where possible.

Fluentd: An effective solution to centralize and transport logs from multiple sources into Dynatrace or a centralized log management system.

2. Data Processing and Storage Layer

This layer processes the incoming data streams and ensures that they are stored efficiently for further analysis, visualization, and long-term retention while minimizing cost impacts.

2.1 Time-Series Databases

Open Source Storage Solutions: Tools such as InfluxDB or TimescaleDB are considered for storing time-series data in a scalable manner. They provide robust data retention policies that help manage and optimize the storage of observability data.

2.2 Message Brokers and Stream Processing

Apache Kafka: Serves as the backbone for real-time data pipelines, ensuring that telemetry data flows reliably from the collection layer to the storage layer.

Apache Beam or Apache Spark: These are used for on-the-fly data processing and transformation, offering enhanced analytics capabilities for proactive monitoring.

2.3 Object Storage Solutions

Open Source Object Storage: For storing log files, raw telemetry data, and backup data, solutions such as MinIO or Ceph provide cost-effective and scalable storage options.

3. Observability and ITSM Layer

This layer delivers comprehensive visibility and incident management by integrating observability tools with ITSM capabilities.

3.1 Dynatrace as the Primary Observability Tool

Core Observability: Dynatrace provides AI-driven monitoring, application performance management, and automated root cause analysis. It is deployed across both on-premise environments and cloud platforms, enabling deep visibility into every component of an enterprise's infrastructure.

Agent-Based and Agentless Monitoring: Where possible, Dynatrace OneAgent is installed on servers and applications. In situations where agent deployment is impractical, collectors and log shippers retrieve the data via APIs or webhooks.

3.2 Datadog for Complementary Cloud Monitoring

Specialized Monitoring: A large Datadog footprint is maintained to complement Dynatrace, particularly in cloud environments where Datadog’s integrations can provide deep insights into containerized and serverless architectures.

Integration with Dynatrace: Datadog is integrated with Dynatrace to provide a unified dashboard, ensuring seamless correlation of events and metrics across platforms.

3.3 BMC Helix for IT Service Management (ITSM)

Unified Incident Management: BMC Helix is deployed as the backbone for IT service management, automating incident responses and change management. It is fully integrated with both Dynatrace and Datadog to correlate observability data with actionable ITSM workflows.

Automation and AIOps: BMC Helix leverages AI to predict, detect, and automatically resolve incidents. Integration with observability tools ensures that alerts trigger ITSM actions, reducing downtime and enhancing operational efficiency.

3.4 Integration and APIs

Inter-Component Communication: All parts of the architecture are connected via robust APIs. Dynatrace, Datadog, and BMC Helix integrate using RESTful APIs and webhooks, ensuring data flows seamlessly between monitoring and ITSM systems.

Message Brokers: Apache Kafka is used to bridge gaps between data collection and processing, and to ensure that notifications and alerts from the observability tools are communicated effectively to the ITSM layer.

Cost Optimization and Open Source Integration

An integral goal of this architecture is to minimize costs while maximizing observability. The approach includes:

Cost Minimization Strategies

Data Retention Policies: Configure data retention policies within Dynatrace, time-series storage solutions, and logging systems to ensure that only essential data is stored. This limits storage costs and optimizes performance.

Consolidation of Platform Footprint: By leveraging open source tools such as Prometheus for metrics collection, Grafana for visualization, and Fluentd for log aggregation, the architecture reduces reliance on high-cost licensed solutions.

Cloud Resource Optimization: Utilize cloud-native cost management tools to maximize the efficiency of hyperscaler resources. This involves auto-scaling, rightsizing, and periodic review of resource utilization.

Open Source Opportunities

OpenTelemetry: Standardizes telemetry data collection from various sources, reducing integration complexity and vendor lock-in.

Prometheus and Grafana: Provide a low-cost yet powerful solution for real-time metrics monitoring and visualization.

Fluentd: Offers a robust solution for log management that can integrate directly with Dynatrace and other systems.

Apache Kafka, Beam, and Spark: These open source technologies are excellent for building robust data pipelines that scale with enterprise demands and facilitate real-time analytics.

Implementation Roadmap

Implementing the proposed architecture is best approached in phases, ensuring that the transition is smooth and that critical systems remain operational.

Phase 1: On-Premise Systems Integration

Deploy Dynatrace OneAgent on mainframes, midrange servers, and legacy systems. Utilize open source data collectors such as Fluentd and OpenTelemetry to bridge monitoring on systems where agent installation isn’t possible.

Initiate the integration process with BMC Helix using their intelligent integrations to map out dependencies and trigger ITSM workflows.

Phase 2: Cloud Environment Integration

Extend observability into AWS, Azure, and GCP. Implement agent-based and agentless monitoring solutions, using native integrations for rapid deployment. Maintain a large footprint of Datadog to capture cloud-specific insights especially in containerized applications or microservices architectures.

Link cloud metrics to the centralized ITSM dashboard through BMC Helix, ensuring that alerts and incidents are synchronized across environments.

Phase 3: Data Processing and Storage Setup

Establish the data processing layer using Apache Kafka for message brokering. Deploy open source time-series databases such as InfluxDB or TimescaleDB, alongside object storage solutions like MinIO, for efficient, scalable data storage.

Implement Apache Beam or Spark for processing streams of observability data in real time, setting up dashboards in Grafana for ad-hoc queries and visual analytics.

Phase 4: Full Integration and Automation

Finalize integrations among Dynatrace, Datadog, and BMC Helix by implementing RESTful APIs and webhooks across the platforms. Configure alerting systems to trigger automated ITSM actions through BMC Helix AIOps and other incident management workflows.

Conduct comprehensive end-to-end testing to ensure seamless interoperability among legacy systems, cloud services, and the ITSM layer.

Comprehensive Component Summary Table

Component Layer	Key Tools and Technologies	Purpose
Data Collection	Dynatrace OneAgent, OpenTelemetry, Prometheus, Fluentd	Collect telemetry from on-premise mainframes and cloud hyperscalers; standardize data collection and logging.
Data Processing & Storage	Apache Kafka, InfluxDB / TimescaleDB, MinIO / Ceph, Apache Beam / Spark	Process, transform and store time-series data and logs efficiently.
Observability & ITSM	Dynatrace, Datadog, BMC Helix	Provide real-time monitoring, AI-driven insights, and manage ITSM operations including incident, problem, and change management.
Integration & Automation	RESTful APIs, Webhooks, Message Brokers	Enable seamless communication and data sharing between observability platforms and ITSM systems.