Data Overview

Bugsnag exposes data across error monitoring, team management, and release tracking. Through Coupler.io, you can export any of these entities and combine them to build a complete picture of your application's stability.

Entities and what they contain

Entity	What you get
Organizations	Organization ID, name, slug, billing plan, creation date
Projects	Project name, type, API key, language, error count, URL
Errors	Error class, message, severity, status, first/last seen timestamps, event count
Events	Full event payload — stack trace, device info, user info, breadcrumbs, timestamps
Pivots	Aggregated breakdowns of error data by dimension (browser, OS, app version, etc.)
Releases	App version, release stage, build tool, source map status, release timestamp
Saved searches	Search name, query filters, associated project
Saved searches usage summaries	Search ID, query count, last used timestamp
Collaborators	User name, email, role, two-factor status, project membership
Teams	Team name, ID, project assignments, member count
Event fields	Custom field names and values attached to error events
Trace fields	Custom field names and values for trace-level data
Supported integrations	Integration name, type, configuration status

Key metrics and dimensions

Error-level metrics

Metric	Description
Event count	Total number of times an error has occurred
User count	Number of unique users affected by an error
First seen	Timestamp when the error was first captured
Last seen	Timestamp of the most recent occurrence
Severity	Error severity level (error, warning, info)
Status	Current status of the error (open, fixed, ignored, snoozed)

Event-level dimensions

Dimension	Description
App version	The release version of the app when the event occurred
Release stage	Environment label (production, staging, development, etc.)
OS name / version	Operating system of the affected device or browser
Browser name / version	Browser context for web errors
Country	Country associated with the user session
User ID / email	Identifies which user experienced the event
Timestamp	Exact date and time the event was captured

Release metrics

Metric	Description
Sessions received	Number of sessions recorded for this release
Crashes received	Number of crashes attributed to this release
Unhandled error rate	Percentage of sessions that had an unhandled error
Release stage	Which environment this release was deployed to

Common metric combinations

• Errors + Releases — join these two entities to see which app versions introduced or resolved specific errors. Use Coupler.io's Join transformation to match on app version.

• Events + Event fields — combine raw events with custom metadata to filter by your own business-specific attributes (e.g., subscription tier, feature flag).

• Errors (multiple projects) — use Append to stack error data from multiple Bugsnag projects into a single table for cross-project stability reporting.

• Pivots — use pivot data to build aggregated views by OS, browser, or app version without processing raw events yourself.

Use cases by role

Engineering

• Monitor error rates by release to catch regressions immediately after deploys

• Join Errors and Releases to correlate crash spikes with specific app versions

• Export Events to BigQuery for long-term retention and custom SQL analysis

• Use Pivots to break down error frequency by OS or browser without manual grouping

Product & QA

Engineering Managers

Platform-specific notes

• The Events entity can return large volumes of data — set a narrow start date if you're pulling from a high-traffic project

• Pivots require a project context and return pre-aggregated data; they are not raw event records

• Saved searches and their usage summaries are project-scoped — you'll need to run separate data flows per project if you have many

• API access and available entities may vary depending on your Bugsnag plan; Enterprise plans have broader API access

• The Personal Auth Token grants access to all organizations and projects your Bugsnag user can see — use a dedicated service account token for production data flows