Delighted + Bruin

Source

Ingest Delighted 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 Delighted responses with CRM and billing data. See which customer segments are unhappy, and how much revenue is at risk.
NPS/CSAT trends, automated
Delighted 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 Delighted 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 Delighted responses. Re-runs and retries are always safe.
YAML-defined, Git-versioned
Your Delighted 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

Delighted account with API access

API key from Delighted settings

Step 1

Add your Delighted 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_keyDelighted API key for authentication

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

Step 2

Create your pipeline

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

Available tables

responsespeoplemetricssurveysbounces

name: raw.delighted_responses
type: ingestr

parameters:
  source_connection: delighted
  source_table: 'responses'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Delighted 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.delighted_responses

Step 4

Run it

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

$ bruin run .

Running pipeline...

  delighted_responses
    ✓ 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

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Canny

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ProductBoard

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Ready to connect Delighted?

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