> For the complete documentation index, see [llms.txt](https://docs.coupler.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coupler.io/sources/category/project-management/codefresh/data-overview.md).

# 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

{% tabs %}
{% tab title="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
  {% endtab %}

{% tab title="DevOps / Platform teams" %}

* Audit agent activity and execution contexts to ensure runtime infrastructure is healthy
* Review Helm repo configurations and context settings to maintain consistent environments
* Export audit logs to BigQuery or a SIEM tool for compliance and incident investigation
  {% endtab %}

{% tab title="Engineering managers" %}

* Use Analytics reports in Google Sheets or Looker Studio to report on deployment frequency and change failure rate
* Feed build data into ChatGPT or Gemini to generate narrative summaries of team delivery health
* Track pipeline trends by project to identify teams that may need additional tooling support
  {% endtab %}
  {% endtabs %}

## 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


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