In the following mind map, I attempted to visualize some core concepts of OpenTelemetry The purpose of OpenTelemetry is to collect, process and export signals. The signals (telemetry data) which OpenTelemetry currently supports can be found on the right side of the mind map:

• Signals:
  • Traces
  • Metrics
  • Logs

The mind map illustrates the components that make up these signals and how they are interconnected.

To learn more about OpenTelemetry:

• Official docs: What is OpenTelemetry?
• A very helpful resource to getting started with OpenTelemetry is the following course from the Linux Foundation: Getting Started with OpenTelemetry

OpenTelemetry Concepts mind map

This mind map provides a detailed overview of the GenAI capabilities available in Azure Databricks.

Also, check out the Azure Databricks Key Concepts mind map

GenAI capabilities Azure Databricks mind map

This mind map offers a comprehensive overview of the core components of MLflow. An interesting fact about MLflow is that it was originally developed by Databricks, with which it maintains strong integration.

MLflow Mind Map

Download the mind map in multiple formats:
MindMap PNG
MindMap JPG
MindMap SVG

As part of my recent studies about Azure Kubernetes, I’ve created a mindmap that outlines and connects some of its core concepts. This mindmap serves as a visual guide to help you navigate the powerful capabilities of Azure Kubernetes and understand how its key components interrelate.

Azure Kubernetes Mind Map

As part of my recent deep dive into Azure API Management, I’ve created a mindmap that outlines and connects some of its core concepts. This mindmap serves as a visual guide to help you navigate the powerful capabilities of Azure API Management and understand how its key components interrelate.

Azure API Management Mind Map

As part of my recent deep dive into Azure Databricks, I’ve created a mindmap that outlines and connects some of its core concepts. This mindmap serves as a visual guide to help you navigate the powerful capabilities of Databricks and understand how its key components interrelate.

Explore essential areas like:

• 📊 Data Management 🗄️: Centralized governance, cataloging, and access control.
• 🛠️ Data Engineering 📊: Tools for building data pipelines, notebooks, and version control.
• 🏢 Data Warehousing 📊: Efficient data storage, querying, and visualization for business insights.
• ⚙️ Computation Management 🖥️: Managing clusters, workflows, and computation resources.
• 🤖 AI and Machine Learning 🧠: Machine learning runtimes, experiments, and model deployment.
• 🔌 Interfaces: Accessing Databricks through UI, APIs, and CLI.

Azure Databricks Mind Map