Inside Dataverse Logs: How Microsoft Dataverse Tracks Activity and Ensures Reliability

In Dataverse, logs capture system, application, and user activities that are essential for both technical operations and business governance. Technically, logs record details such as data changes (audit logs), background processes like workflows and Power Automate runs (system jobs), plugin execution traces, and integration/API calls, which help developers and administrators troubleshoot issues, analyze performance, and ensure system stability. From a business perspective, logs support compliance, security, and accountability by providing an audit trail of who did what and when, which is critical for regulated industries. Logically, they act as the platform’s “memory,” enabling visibility into system behavior, supporting root-cause analysis, and helping organizations make informed decisions about capacity planning, optimization, and risk management.

What is a Log in Dataverse?

In Dataverse, a log is a system record that captures what happens inside the platform when users or automated processes work with data.
Logs are created automatically by Dataverse to track actions, changes, and background operations.
  • Logs are not business data (like accounts or cases).
  • They are system and operational data used for monitoring, auditing, troubleshooting, and compliance.
Why Dataverse Uses Logs

Dataverse uses logs to maintain visibility, control, and reliability across the platform as data and processes change continuously. Logs record system activities such as data updates, automated processes, integrations, and background operations, allowing organizations to track what happened, when it happened, and why it happened.

Dataverse logs help the platform to:
  • Track data changes
  • Record system activity
  • Support auditing and compliance
  • Help with error investigation
  • Monitor background processes
You usually don’t see logs in daily business use, but Dataverse depends on them heavily.
Types of Logs in Dataverse

1. Audit Logs
Audit logs record who changed what and when.

They capture:
  • Record creation
  • Updates (old value → new value)
  • Deletions
  • User and timestamp
Used mainly for security, compliance, and traceability.

2. Plugin Trace Logs

Generated when plugins run and write trace messages.

They include:
  • Execution details
  • Error messages
  • Debug information
=> Very useful for troubleshooting
=> Should be limited in production

3. Workflow and Flow Logs

Created by:
  • Classic workflows
  • Power Automate flows triggered by Dataverse
They record:
  • Run history
  • Success or failure status
  • Execution details

4. System Job Logs (Async Operations)

Dataverse logs background jobs such as:
  • SLA processing
  • Bulk updates
  • Imports and exports
  • Scheduled tasks
These run silently but generate logs continuously.

5. SLA and KPI Logs

Generated when:
  • SLAs are applied
  • KPI timers start, pause, or complete
  • SLA instances are recalculated
Common in Customer Service scenarios.

6. Other System Logs

Includes:
  • Duplicate detection logs
  • Data import logs
  • Solution import logs
  • Platform diagnostics

Where Dataverse Stores Logs
  • Logs are stored in Dataverse Log Capacity
  • Measured in GB
  • Shared across the entire tenant
  • Managed mainly by the system (not users)

You can view usage in:


How Logs Are Created

Logs are generated when:
  • A user creates or updates a record
  • A plugin executes
  • A flow runs
  • A workflow starts or ends
  • SLA rules are evaluated
  • System jobs run in the background
Even small actions can generate multiple log entries.

Why Logs Matter

If logs are not managed:
  • Storage fills up silently
  • System performance may degrade
  • Plugins and automations may fail
  • SLA and background jobs may stop working
Simple Example

A user updates a Case



One action → multiple logs

In Easy Words

Dataverse logs are the activity history of the system. They help track changes, detect issues, and keep the platform reliable—but they must be monitored and cleaned regularly.

Summary:
Dataverse logs capture critical information such as data changes, automated processes, plugin executions, and background jobs, enabling visibility into how the system operates behind the scenes. These logs play a vital role in ensuring compliance, supporting audits, diagnosing issues, and maintaining performance as usage grows. By effectively tracking activity and system behavior, Dataverse logs help organizations proactively manage risks, troubleshoot faster, and ensure reliable business operations at scale.

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