Birdeye + Bruin

Source

Ingest Birdeye 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

Feedback tied to revenue
Join Birdeye responses with CRM and billing data. See which customer segments are unhappy, and how much revenue is at risk.
NPS/CSAT trends, automated
Birdeye response data flows into your warehouse. Trend dashboards update automatically, no manual exports.
Response quality validation
Quality checks catch duplicate submissions, incomplete responses, and stale data before it reaches your reports.
Cross-source customer insight
Combine Birdeye feedback with support tickets, product usage, and sales data for a complete customer picture.

For data & engineering teams

How it works

Response deduplication
Bruin's merge strategy prevents duplicate Birdeye responses. Re-runs and retries are always safe.
YAML-defined, Git-versioned
Your Birdeye pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Completeness validation
Quality checks catch incomplete responses, missing timestamps, and null IDs on every sync.
Transform responses in SQL
Calculate NPS, CSAT, and sentiment metrics with SQL transforms, in the same pipeline as ingestion.

Before you start

Birdeye account with API access

API key from Birdeye dashboard

Step 1

Add your Birdeye connection

Connect using API key authentication. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyBirdeye API key for authentication

connections:
  birdeye:
    type: birdeye
    uri: "birdeye://?api_key=<api-key>"

Step 2

Create your pipeline

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

Available tables

reviewslistingssurveyscontactsconversations

name: raw.birdeye_reviews
type: ingestr

parameters:
  source_connection: birdeye
  source_table: 'reviews'
  destination: bigquery

Step 3

Add quality checks

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

Catch duplicate survey responses

Validate submission timestamps are recent

Ensure response IDs are unique across syncs

columns:
  - name: response_id
    checks:
      - name: not_null
      - name: unique
  - name: submitted_at
    checks:
      - name: not_null

custom_checks:
  - name: responses are recent
    query: |
      SELECT MAX(submitted_at) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
      FROM raw.birdeye_reviews

Step 4

Run it

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

$ bruin run .

Running pipeline...

  birdeye_reviews
    ✓ 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 Surveys & Feedback integrations

Canny

Delighted

Hotjar

Pendo

ProductBoard

Qualtrics

Ready to connect Birdeye?

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