Support Policy - experiment, share feedback, and help shape the future
This repository is part of an enablement project created by the Center of Excellence at Dynatrace. Our mission is to empower you to explore and adopt these resources to accelerate innovation. Support is community-driven and provided exclusively via GitHub Issues.
We will make every effort to assist and address reported problems, but please note:
- The materials are provided “as-is”, without any warranties or guarantees.
- Use of this technology is at your own discretion and risk.
We encourage you to experiment, share feedback, and help shape the future. Start building today!
This Codespace leverages the Dynatrace Enablement Framework, providing a robust and flexible development environment. Key features include:
- Seamless operation within GitHub Codespaces, as a remote container, or locally via Docker.
- Cross-compilation support for both AMD and ARM architectures, ensuring broad compatibility.
- Adherence to industry standards and best practices to optimize the developer experience.
- Real-time observability of Kubernetes clusters using Dynatrace Full-Stack monitoring.
- Integrated Dynatrace MCP Server to deliver deep, actionable insights across distributed systems.
To learn more about the Dynatrace Enablement Framework and how it can enhance your development workflow, please refer to the official documentation
DQL Fundamentals#
Welcome to the Dynatrace Query Language (DQL) Fundamentals workshop.
Overview#
This hands-on lab teaches you how to query, filter, summarize, and visualize data in Dynatrace using DQL through interactive Notebooks. Through 7 progressive exercise modules and 3 real-world use cases, you'll master the universal language for querying all observability data in the Dynatrace platform.
What you'll learn#
- Logs — Fetch, filter, parse, and visualize log data (Parts 1-3)
- Metrics — Query timeseries, CPU usage, and use Davis forecasting
- Events — Analyze Davis problems, entity relationships, and vulnerabilities
- Business Events — Trading data analysis with summarization and JSON parsing
- CoPilot — Use Davis CoPilot for AI-assisted DQL queries
Prerequisites#
Requirements
- A Grail enabled Dynatrace SaaS Tenant (sign up here).
- A GitHub account to interact with the demo repository.
How it works#
- Download the Exercise Notebooks from the Exercises folder
- Upload them to your Dynatrace environment as Notebooks
- Follow the exercises — each has empty DQL query sections for you to complete
- When done, upload the corresponding Answer Notebook to check your work
Lab structure#
| # | Topic | Skills |
|---|---|---|
| 1 | Logs Part 1 | fetch, filter, timeframes, fieldsAdd, fieldsRemove |
| 2 | Logs Part 2 | summarize, sort, limit, aggregation functions |
| 3 | Logs Part 3 | parse, data extraction, visualization |
| 4 | Metrics | timeseries, filter, Davis forecasting |
| 5 | Events | fetch events, entity traversal, lookup, vulnerability analysis |
| 6 | Business Events | fetch bizevents, trading analysis, JSON parsing |
| 7 | CoPilot | AI-assisted DQL with Davis CoPilot |
| 8 | Use Cases | Real-world scenarios with sample data |