DQL Exercises#
Now that logs are flowing into Dynatrace from our Kubernetes environment, it's time to unlock their full potential. Enter Dynatrace Query Language (DQL) - a powerful, intuitive language purpose-built for observability at scale. With DQL, you can slice through massive volumes of log data with precision, filter by meaningful attributes, extract insights in seconds, and even correlate logs with traces and metrics—all in a single query. Whether you're troubleshooting an issue, hunting for anomalies, or building dashboards, DQL makes it easy to ask complex questions and get clear answers fast. Let’s dive in and see how DQL transforms raw log data into actionable intelligence.
Access the hands-on exercises for DQL from the Notebook in your Dynatrace tenant. In your Dynatrace tenant, open the Notebooks App. Locate the Notebook titled Workshop - Workshop Exercises
. We will be completing the DQL Exercises
linked Notebooks.
Logs DQL 101#
Complete the DQL exercises found in the Notebook Workshop - Logs DQL 101 - Exercises
.
Reference the Notebook Workshop - Logs DQL 101 - Answer Key
as needed or upon completion.
Logs DQL 102#
Complete the DQL exercises found in the Notebook Workshop - Logs DQL 102 - Exercises
.
Reference the Notebook Workshop - Logs DQL 102 - Answer Key
as needed or upon completion.
Logs DQL 201#
Complete the DQL exercises found in the Notebook Workshop - Logs DQL 201 - Exercises
.
Reference the Notebook Workshop - Logs DQL 201 - Answer Key
as needed or upon completion.
Log Metrics#
Complete the DQL exercises found in the Notebook Workshop - Log Metrics - Exercises
.
Reference the Notebook Workshop - Log Metrics - Answer Key
as needed or upon completion.
CoPilot Queries#
Complete the DQL exercises found in the Notebook Workshop - CoPilot - Exercises
.
Reference the Notebook Workshop - CoPilot - Answer Key
as needed or upon completion.
Continue#
In the next section, we'll learn anomaly detection strategies from log data using the Davis AI.