PostgreSQL + Bruin
Ingest data from PostgreSQL or push enriched data back — with quality checks, lineage, and scheduling. Defined in YAML, version-controlled in Git.
For business teams
What you get
Real-time warehouse sync
PostgreSQL tables replicate to your warehouse continuously. Analytics teams work with fresh data, not yesterday's export.
Catch issues at the source
Quality checks validate PostgreSQL data as it replicates. Null IDs, duplicate records, and schema drift get caught early.
Multi-source joins
Combine PostgreSQL with SaaS data, APIs, and other databases in your warehouse. One Bruin pipeline handles it all.
No untracked scripts
Replication is defined in YAML, reviewed in PRs, and deployed with CI/CD. No more mystery cron jobs.
For data & engineering teams
How it works
CDC with merge strategy
Bruin handles change data capture from PostgreSQL with deduplication. Schema changes are detected and handled automatically.
YAML-defined, Git-versioned
Your PostgreSQL replication is a YAML file. Review in PRs, deploy with CI/CD. No more untracked database scripts.
Row-level quality checks
Validate primary keys, foreign keys, and referential integrity on every sync. Catch corruption at the source.
Multi-source pipelines
Combine PostgreSQL with SaaS APIs and other databases in one pipeline. Bruin resolves cross-source dependencies.
Before you start
Step 1
Add your PostgreSQL connection
PostgreSQL connection using standard connection string format. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.
Parameters
usernameDatabase userpasswordUser passwordhostDatabase server hostname or IPportServer port (default 5432)databaseDatabase namesslmodeSSL mode (disable, require, verify-ca, verify-full)
connections:
postgresql:
type: postgresql
uri: "postgresql://username:password@host:port/database?sslmode=disable"Step 2
Create your pipeline
Define a YAML asset that tells Bruin what to pull from PostgreSQL and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.
Available tables
name: raw.postgresql_public.users
type: ingestr
parameters:
source_connection: postgresql
source_table: 'public.users'
destination: bigqueryStep 3
Add quality checks
Add column-level and custom SQL checks to your PostgreSQL data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.
columns:
- name: id
checks:
- name: not_null
- name: unique
- name: created_at
checks:
- name: not_null
custom_checks:
- name: row count within expected range
query: |
SELECT COUNT(*) BETWEEN 1 AND 10000000
FROM raw.postgresql_public.usersStep 4
Run it
One command. Bruin connects to PostgreSQL, 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.
--start-date$ bruin run .Running pipeline...
postgresql_public.users
✓ 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 12sOther Database integrations
Ready to connect PostgreSQL?
Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.