Confluence + Bruin

Source

Ingest Confluence data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Operational analytics
Confluence data in your warehouse means analytics that Confluence's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Confluence with Jira, GitHub, Slack, and other tools. One dashboard that shows the real state of projects.
Team workload insights
Understand collaboration patterns, bottlenecks, and workload distribution from Confluence data — automatically updated.
No manual data pulling
Confluence data syncs on schedule. Managers and leads get fresh data without asking anyone.

For data & engineering teams

How it works

Incremental sync
Only sync new and changed Confluence records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Confluence pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Confluence schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Confluence data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Confluence Cloud account

Atlassian API token

Read access to spaces

Step 1

Add your Confluence connection

Connect using Atlassian API token. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

emailAtlassian account email
api_tokenAtlassian API token
instanceAtlassian Cloud instance hostname

connections:
  confluence:
    type: confluence
    uri: "confluence://email:[email protected]"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Confluence and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

pagesspacesblog_postscommentsattachments

name: raw.confluence_pages
type: ingestr

parameters:
  source_connection: confluence
  source_table: 'pages'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Confluence data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate workspace data synced completely

Ensure record IDs are unique and titles are present

Catch missing or null fields on every sync

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: title
    checks:
      - name: not_null

custom_checks:
  - name: workspace sync is complete
    query: |
      SELECT COUNT(*) > 0
      FROM raw.confluence_pages

Step 4

Run it

One command. Bruin connects to Confluence, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Confluence to your dashboards

$ bruin run .

Running pipeline...

  confluence_pages
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

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Ready to connect Confluence?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs