Log Management and Analytics Tour

Precise answers from unified data in context

See how we're different

1/11

Dynatrace ingests and analyzes petabytes of log data into its data lakehouse, Grail. The context of observability and security data is automatically and efficiently retained, serving as the single log management and analytics platform to bring your IT, security, and business teams together to meet your service level objectives.

Dashboard

Thank you for going through the tour! As you can see, our unique approach to enterprise log management and analytics automatically provides log data, metrics, and analysis in the context of topology, traces, and user sessions – with no schemas or storage tiers to manage!

Precise answers from unified data in context

Thank you for your feedback!

Did you find this product tour helpful?

Explore our unique approach to log management and analytics to see how Dynatrace is the only platform to provide log data, metrics, and analysis in the context of topology, traces, and user sessions – all with no schemas or storage tiers to manage.

Request demo

Free trial

Download product brief

Request a live demo

LOG MANAGEMENT AND ANALYTICS

Logs are integrated everywhere within the platform so any user you choose can have access for troubleshooting and analysis.

2/11

Let's start with a simple use case. From the log viewer, examine logs within the production namespace, for a Kubernetes frontend deployment, and log level of ERROR.

3/11

Refine results further using any combination of the available log attributes which are automatically-detected or custom-defined.

4/11

Click on a log row to view all the log attributes. Any attribute can be quickly added to the filter.

5/11

Topology-based attributes are also automatically connected to log records and are hyperlinked to the associated host, process, cluster, or service view. No tagging required.

6/11

Now, navigate to the trace associated with this log event.

7/11

The trace view helps you understand code dependencies, timing, errors, and logs in context of each request in the flow.

8/11

For Advanced users, the Dynatrace Query Language (DQL) offers maximum flexibility and analyzes all types of observability data in a way that is easy to read, author, and automate.

9/11

For example, this query gets a count of created, updated, and deleted configurations by cluster tenant and user – all on-demand with no indexing or rehydration.

10/11

Before we explore how to use DQL for advanced analytics, let's quickly look at how logs support troubleshooting with the Dynatrace software intelligence platform.

11/11

Here on a Problem Card, review the business impact, affected SLOs, and root cause detected by Dynatrace’s causal AI engine, Davis.

1/3

Davis has detected the root cause of this problem as a Failure Rate Increase.

2/3

After doing simple log filtering in this view, let's do advanced log analysis with DQL.

3/3

The DQL query is pre-loaded, but we want to build out the statement to summarize how many errors were occurring by day.

1/7

First, the error message we want is within the original log content and with DQL we can easily extract this with no upfront definition. With other tools, you are forced to define a new custom field and ingest the logs again.

2/7

When the query is run, the error message is now parsed into its own field, demonstrating the power of schema-on-read.

3/7

Next, isolate the error message using the PARSE command and get a count with the SUMMARIZE command.

4/7

If you like, pin the chart to a dashboard and share the results with your team.

6/7

Lastly, adjust the SUMMARIZE command to group by day, and choose bar chart visualization.

5/7

Log data is displayed on your dashboard with data from your SLOs, custom metrics, applications, services, and user sessions to keep tabs on overall performance.

7/7

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

Advanced analytics

Troubleshooting basics

Observability overview

1/21

Observability overview

Logs are integrated everywhere within the platform so any user you choose can have access for troubleshooting and analysis.

2/21

Observability overview

Let's start with a simple use case. From the log viewer, examine logs within the production namespace, for a Kubernetes frontend deployment, and log level of ERROR.

3/21

Observability overview

Refine results further using any combination of the available log attributes which are automatically-detected or custom-defined.

4/21

Observability overview

Click on a log row to view all the log attributes. Any attribute can be quickly added to the filter.

5/21

Observability overview

Topology-based attributes are also automatically connected to log records and are hyperlinked to the associated host, process, cluster, or service view. No tagging required.

6/21

Observability overview

Now, navigate to the trace associated with this log event.

7/21

Observability overview

The trace view helps you understand code dependencies, timing, errors, and logs in context of each request in the flow.

8/21

Observability overview

For Advanced users, the Dynatrace Query Language (DQL) offers maximum flexibility and analyzes all types of observability data in a way that is easy to read, author, and automate.

9/21

Observability overview

For example, this query gets a count of created, updated, and deleted configurations by cluster tenant and user – all on-demand with no indexing or rehydration.

10/21

Observability overview

Before we explore how to use DQL for advanced analytics, let's quickly look at how logs support troubleshooting with the Dynatrace software intelligence platform.

11/21

Troubleshooting basics

Here on a Problem Card, review the business impact, affected SLOs, and root cause detected by Dynatrace’s causal AI engine, Davis.

12/21

Troubleshooting basics

Davis has detected the root cause of this problem as a Failure Rate Increase.

13/21

Troubleshooting basics

After doing simple log filtering in this view, let's do advanced log analysis with DQL.

14/21

Advanced analytics

The DQL query is pre-loaded, but we want to build out the statement to summarize how many errors were occurring by day.

15/21

Advanced analytics

16/21

Advanced analytics

When the query is run, the error message is now parsed into its own field, demonstrating the power of schema-on-read.

17/21

Advanced analytics

Next, isolate the error message using the PARSE command and get a count with the SUMMARIZE command.

18/21

Advanced analytics

Lastly, adjust the SUMMARIZE command to group by day, and choose bar chart visualization.

19/21

Advanced analytics

If you like, pin the chart to a dashboard and share the results with your team.

20/21

Advanced analytics

Log data is displayed on your dashboard with data from your SLOs, custom metrics, applications, services, and user sessions to keep tabs on overall performance.

21/21