LogRocket + Bruin

Source

Ingest LogRocket 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

Analysis beyond built-in reports
Join LogRocket behavioral data with revenue, support, and CRM data. Answer questions LogRocket alone can't.
Trusted behavioral data
Quality checks catch tracking gaps, duplicate events, and missing timestamps before they corrupt your models.
Self-serve for analysts
LogRocket data lands in your warehouse where analysts already work. No more exporting, no more waiting.
Real user journeys
Combine LogRocket events with purchase and support data to see the full customer journey, not just the product funnel.

For data & engineering teams

How it works

Event schema validation
Check for null event IDs, missing timestamps, and duplicate events on every sync. Catch tracking issues at ingestion.
YAML-defined, Git-versioned
Your LogRocket pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
SQL + Python transforms
Transform raw LogRocket events into funnels, cohorts, and user journeys with SQL or Python, in the same pipeline.
Dependency-aware scheduling
Bruin resolves pipeline dependencies automatically. Transforms only run after LogRocket data has landed.

Before you start

LogRocket account with API access

LogRocket project configured with SDK

Step 1

Add your LogRocket connection

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

Parameters

api_keyLogRocket API key from project settings
app_idYour LogRocket application identifier (org/app format)

connections:
  logrocket:
    type: logrocket
    uri: "logrocket://?api_key=<your-api-key>&app_id=<your-app-id>"

Step 2

Create your pipeline

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

Available tables

sessionseventsuserserrorspages

name: raw.logrocket_sessions
type: ingestr

parameters:
  source_connection: logrocket
  source_table: 'sessions'
  destination: bigquery

Step 3

Add quality checks

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

Catch duplicate events and missing timestamps

Validate event freshness, stale data gets flagged

Ensure event IDs are unique across syncs

columns:
  - name: event_id
    checks:
      - name: not_null
      - name: unique
  - name: event_timestamp
    checks:
      - name: not_null

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

Step 4

Run it

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

$ bruin run .

Running pipeline...

  logrocket_sessions
    ✓ 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 Analytics integrations

Amplitude

Apple App Store

Braze

ChartMogul

CleverTap

Datadog

Ready to connect LogRocket?

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