Tableau + Bruin

Source

Ingest Tableau 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

Dashboards powered by clean data
Feed Tableau from Bruin pipelines with built-in quality checks. No more stale or incorrect dashboards.
End-to-end lineage to ${pn}
Trace data from raw sources through every transformation to Tableau. Find the root cause of wrong numbers in seconds.
Scheduled data refresh
Bruin pipelines refresh Tableau data on a cron. Dependencies resolve automatically — Tableau only sees complete data.
Multi-source, one destination
Combine 100+ sources into clean models that power Tableau. Bruin handles the pipeline, Tableau handles the visuals.

For data & engineering teams

How it works

Dependency-aware scheduling
Bruin ensures upstream transforms complete before Tableau gets new data. No more stale or partial dashboards.
YAML-defined, Git-versioned
Every pipeline feeding Tableau is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Quality gates before visualization
Quality checks run before data reaches Tableau. If checks fail, Tableau keeps showing the last known good data.
End-to-end lineage
Trace data from raw sources through transforms to Tableau dashboards. Find root causes in seconds, not hours.

Before you start

Tableau Server or Cloud account

Personal access token created

Site admin or viewer role

Step 1

Add your Tableau connection

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

Parameters

token_nameTableau personal access token name
token_secretTableau personal access token secret
server_urlTableau Server or Cloud URL
site_idTableau site content URL name

connections:
  tableau:
    type: tableau
    uri: "tableau://token_name:token_secret@server_url/site_id"

Step 2

Create your pipeline

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

Available tables

workbooksviewsdatasourcesprojectsuserssubscriptions

name: raw.tableau_workbooks
type: ingestr

parameters:
  source_connection: tableau
  source_table: 'workbooks'
  destination: bigquery

Step 3

Add quality checks

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

Validate data freshness before it reaches dashboards

Ensure IDs are unique across syncs

Block stale data from appearing in visualizations

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

custom_checks:
  - name: data is fresh
    query: |
      SELECT MAX(updated_at) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
      FROM raw.tableau_workbooks

Step 4

Run it

One command. Bruin connects to Tableau, 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 Tableau to your dashboards

$ bruin run .

Running pipeline...

  tableau_workbooks
    ✓ 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

Other Data Analytics Platform integrations

Looker

Metabase

Power BI

Ready to connect Tableau?

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