Events powered by Grail overview (DPS)

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This page describes the different Events-related capabilities and the features that they provide with a DPS subscription.

For information about how usage of a specific capability translates to consumption of your DPS license commit, see

Dynatrace provides monitoring and reporting of

  • Built-in event types via OneAgent or cloud integrations.

  • Custom events and/or event-ingestion channels. These include

Billable and included events

The consumption of all events is billable, except for certain events that are included with a separate rate-card capability. These exceptions are described in the table below.

Built-in event kind

Relevant capability

Ingest & Process

Retain1

Query

Davis AI problems and events

Full-Stack Monitoring

Included

First 15 months are included

Included

Kubernetes warning events

Kubernetes Platform Monitoring (KPM)

60 warning events per pod-hour are included

First 15 months are included

Queries generated from within Kubernetes (new) Kubernetes are included

Built-in security events

Runtime Vulnerability Analytics (RVA)

Billable

First 3 years are included

Queries generated from within Vulnerabilities Vulnerabilities are included

1

Retention beyond the included timeframe is billable as Events powered by Grail - Retain.

Events - Ingest & Process feature overview

Here's what's included with the Ingest & Process data-usage dimension:

Concept

Explanation

Data delivery

Delivery of events via OneAgent, RUM JavaScript, or Generic Event Ingestion API (via ActiveGate)

Topology enrichment

Enrichment of events with data source and topology metadata

Data transformation

  • Add, edit, or drop any business event attribute
  • Perform mathematical transformations on numerical values (for example, creating new attributes based on calculations of existing fields)
  • Extract business, infrastructure, application, or other data from raw business events. This can be a single character, string, number, array of values, or other. Extracted data can be turned into a new field, allowing additional querying, filtering, etc.
  • Mask sensitive data by replacing specific business attributes with a masked string

Data-retention control

Manage data retention periods of incoming events based on bucket assignment rules

Conversion to timeseries

Create metrics from event attributes (note that creating custom metrics generates additional consumption beyond the consumption for ingestion and processing.)

Events - Retain feature overview

Here's what's included with the Retain data-usage dimension:

Concept

Explanation

Data availability

Retained data is accessible for analysis and querying until the end of the retention period. Events retention is defined at the bucket level, ensuring tailored retention periods for specific events.

Retention periods

Choose a retention period

  • 10 days (10 days)
  • 2 weeks (15 days)
  • 1 month (35 days) (this is the default period)
  • 3 months (95 days)
  • 1 year (372 days)
  • 15 months (462 days)
  • 3 years (1,102 days)
  • 5 years (1,832 days)
  • 7 years (2,562 days)
  • 10 years (3,657 days)

Events - Query feature overview

Query data usage occurs when:

  • Submitting custom DQL queries in the Logs & Events viewer in advanced mode.
  • Business Observability Apps (Business Flow, Salesforce Insights, and Carbon Impact)
  • Executing DQL queries in Notebooks, Dashboards, Workflows, Custom apps, and via API.

Here's what's included with the Query data-usage dimension:

Concept

Explanation

On-read parsing

  • Use DQL to query historical events in storage and extract business, infrastructure, or other data across any timeframe, and use extracted data for follow-up analysis.
  • No upfront indexes or schema required for on-read parsing

Aggregation

Perform aggregation, summarization, or statistical analysis of data in events across specific timeframes or time patterns (for example, data occurrences in 30-second or 10-minute intervals), mathematical, or logical functions.

Reporting

Create reports or summaries with customized fields (columns) by adding, modifying, or dropping existing event attributes.

Context

Use DQL to analyze event data in context with relevant data on the Dynatrace platform, for example, user sessions or distributed traces.

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