Data Overview

Codefresh exposes data across your entire CI/CD operation — from pipeline definitions and build records to audit trails, runtime agents, and Helm repositories. Here's a breakdown of what each entity contains and how you can use it.

Entities at a glance

Entity	Best used for
Builds	Pipeline run history, success/failure rates, duration trends
Pipelines	Pipeline definitions, triggers, and configuration
Audits	Compliance tracking, user activity monitoring
Analytics reports	Aggregated CI/CD metrics over time
Analytics metadata	Understanding report structure and available dimensions
Projects	Organizing and filtering pipelines by project
Agents	Monitoring runtime infrastructure
Contexts	Reviewing shared variables and secrets metadata
Execution contexts	Debugging runtime environments
Accounts	User management and account inventory
Account settings	Configuration auditing
Step types	Cataloging custom pipeline steps
Helm repos	Helm chart repository inventory

Builds

Key fields

Field	Description
Build ID	Unique identifier for the build run
Pipeline name	The pipeline that triggered the build
Status	Outcome: success, failure, error, terminated
Started at	Timestamp when the build began
Finished at	Timestamp when the build completed
Duration	Total execution time
Triggered by	User or event that triggered the build
Branch	Git branch associated with the build
Commit SHA	Git commit hash
Error message	Failure reason if the build did not succeed

Pipelines

Key fields

Field	Description
Pipeline ID	Unique identifier
Name	Pipeline display name
Project	Associated project
Created at	Pipeline creation date
Updated at	Last modification date
Triggers	Events configured to trigger the pipeline
Steps	Step definitions in the pipeline

Audits

Key fields

Field	Description
Event type	Action performed (e.g., pipeline updated, user added)
User	Who performed the action
Timestamp	When the event occurred
Entity type	What was affected (pipeline, account, context, etc.)
Entity ID	Identifier of the affected resource
IP address	Origin IP of the action

Analytics reports

Key fields

Field	Description
Report period	Date range covered by the report
Granularity	Daily, weekly, or monthly aggregation
Total builds	Count of builds in the period
Success rate	Percentage of successful builds
Average duration	Mean build execution time
Pipeline breakdown	Per-pipeline performance metrics

Common metric combinations

• Builds + Pipelines — Join on pipeline ID to enrich build records with pipeline metadata for full-context reporting

• Builds over time — Use Aggregate transformation to calculate daily or weekly success rates from raw build records

• Audits + Accounts — Join on user ID to map audit events to named users for compliance reports

• Analytics reports (Append) — Append reports from multiple date ranges to build a continuous historical dataset

Use cases by role

Engineering leads

• Monitor build success and failure rates across all pipelines to identify flaky tests or unstable stages

• Track average build duration over time to catch performance regressions before they slow down the team

• Compare pipeline activity across projects to balance workload and infrastructure costs

DevOps / Platform teams

Engineering managers

Platform-specific notes

• Build history depth depends on your Codefresh plan — enterprise accounts retain longer build history

• Analytics reports require setting both granularity and date range parameters; missing either may result in empty data

• The Audits entity only captures events within your account's retention window as configured in Codefresh

• Contexts contain metadata about shared variables but do not expose secret values

• Agent data reflects the current state of registered agents and is not historical