MyObservability

My Observability & AIOps

By Sunil Marella

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Topics Covered

• 1. Observability
• 2. AIOps
• 3. Implementing Observability & AIOps

Follow below tutorial to build end to end visibility for your organization to identify issues proactively.

My Observability Design

• Step 1 - Understanding Observability & AIOps: Introduction, OpenTelemetry
• Step 2 - Data Collection: Collect, process, and route observability data using Observability tools agent / vendor agnostic methods from various technologies.
• Step 3 - Observability: Analyze the data, Generate alerts / visualization using observability tools.
• Step 4 - AIOps: Aggregate, correlate, and prioritize incident data. Filter out the alert noise and detect IT system performance issues.
• Step 5 - End-to-End Observability as Code Lifecycle: Managing the complete journey from producing telemetry to using it for operational decisions, with the configuration controlled through code, Git, validation, and automation.
• Step 6 - Manage Telemetry Data - Next Generation Data Transformation: Pre-ingest control and cost optimization along with observability pipeline that routes, transforms, filters, and enriches telemetry data to different destinations in real time
• Step 7 - Model Context Protocol (MCP): Uses AI assistant to expose observability systems as MCP servers to query logs, fetch metrics, or analyze traces using LLM to enable cross-platform, consistent, and secure AI observability workflows

Note on terminology: The terms used in this documentation—Instrumentation as Code, Telemetry as Code, and Observability as Code—represent my way of structuring the end-to-end observability lifecycle.

Implementing Observability & AIOps

Step 1 - Understanding Observability & AIOps

    Visit My Understanding Observability & AIOps Tutorial - Introduction

Step 2 - Data Collection/Edge Processing

    Visit My Open Telemetry Tutorial - Open Telemetry

    Visit My Splunk Edge Processing Tutorial - Splunk Edge Processing

    Visit My Cribl Tutorial - Cribl

Step 3 - Observability Implementation

    Visit My Splunk Observability Tutorial - Splunk Observability

    Visit My Dynatrace Tutorial - Dynatrace

    Visit My NewRelic Tutorial - NewRelic

    Visit My Grafana & Prometheus Tutorial - Grafana & Prometheus

    Visit My OpenSearch Tutorial - OpenSearch

    Visit My Appdynamics Tutorial - Appdynamics

    Visit My Elastic Tutorial - Elastic

    Visit My DataDog Tutorial - DataDog

    Visit My Cribl Tutorial - Cribl

Step 4 - AIOps Implementation

    Visit My AIOps Tutorial - AIOps

    Visit My Moogsoft Tutorial - Moogsoft

    Visit My ServiceNow Tutorial - ServiceNow

    Visit My Bigpanda Tutorial - Bigpanda

    Visit My Splunk ITSI Tutorial - Splunk ITSI

Step 5 - End-to-End Observability as Code Lifecycle

    Visit My Monitoring As Code Tutorial - Monitoring As Code (MaC)

    Visit My Telemetry As Code Tutorial - Telemetry As Code (TaC)

    Visit My Instrumentation As Code Tutorial - Instrumentation As Code (InAC)

Step 6 - Manage Telemetry Data - Next Generation Data Transformation

    Visit My Splunk Edge Processor Tutorial - Splunk Edge Processor

    Visit My Cribl Stream Tutorial - Cribl Stream

Step 7 - Model Context Protocol (MCP)

    Visit My Model Context Protocol Tutorial - MCP

Advanced Topics Information

Splunk

• Observability agent through Splunk deployment server - Splunk Add-on for OpenTelemetry Collector

AI & LLM Observability

• Monitoring AI & LLM Data models using Observability ToolsAI & LLM Observability

Disclaimer: This is purely based on my learning, knowledge and reference from tutorial / documentation.

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My Contact Information

GitHub Linkdin My Page

My Other sites
👉 My Observability My AIOps My A.I. My Architecture

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