Your last
data platform.
Reliable data. 10x faster, 90% less complexity.
Bruin is open-core, you can view it on GitHub .
Trusted by forward-thinking teams
INGEST
Copy data from anywhere
CLI-based ingestion tool built on open-source. Batch and incremental loading. Connect any source to any destination with a single command.
- Multiple sources & destinations.
- Postgres, MySQL, BigQuery, Snowflake, S3, and more.
- Incremental loading.
- Efficient data syncing with snapshot and incremental modes.
- Schema evolution.
- Automatically update schemas in the destination to match the source.
name: raw.users
type: ingestr
parameters:
source_connection: postgres
source_table: 'public.users'
destination: bigquery
TRANSFORM
Pipelines you can trust
Build data pipelines directly from SQL and Python files. Git-native workflows with dependency graphs and environment-aware runs.
- SQL & Python native.
- Write transformations in the language you prefer.
- Column-level lineage.
- Automatically extract the lineage of your transformations.
- Jinja templating.
- Parameterize your pipelines with Jinja templating.
/* @bruinname: dashboard.bookings
owner: [email protected]
materialization:
type: table@bruin */SELECT
bookings.Id AS BookingId,
sessions.Name AS SessionName,
bookings.SessionType AS SessionType
FROM raw.Bookings AS bookings
INNER JOIN raw.Sessions AS sessions
ON bookings.SessionId = sessions.Id
WHERE updated_at BETWEEN '{{ start_date }}' AND '{{ end_date }}'
QUALITY
Data quality built-in
Define quality checks alongside your pipelines. Catch issues before they reach production with comprehensive validation rules.
- Schema validation.
- Column types, primary keys, and accepted values.
- Automated alerts.
- Get automated alerts if quality checks fail via Slack, email, and more.
- Custom SQL checks.
- Define business rules with SQL assertions.
columns:
- name: SessionType
type: STRING
description: Type of training session
checks:
- name: not_null
- name: accepted_values
value:
- 'Group'
- 'Private'TRUSTED BY DATA TEAMS
Ship faster with Bruin
Bruin solved many pain points for my team, making work not just more enjoyable but significantly more productive as well.
With Bruin, what previously took hours can now be accomplished in just 15 minutes.
Thanks to Bruin, we have been able to automate all the manual parts of our data pipelines. We are able to focus on our business users' needs while delivering insights faster than ever before.
Bruin strengthens our data infrastructure, boosting user acquisition accuracy and efficiency.
Bruin's product has effectively addressed all the challenges my team faced in developing, orchestrating, and monitoring our pipelines.
Partnering with Bruin has significantly increased our productivity, making it easy for our data team to manage everything seamlessly.
Bruin's platform is incredibly intuitive. As a new team member, I was able to contribute effectively from day one.
OBSERVABILITY
Cost & performance insights
Track cloud warehouse costs at the asset level. Monitor performance metrics and optimize resource usage across your data stack.
- Asset-level tracking.
- Cost attribution per table, query, and pipeline.
- Usage analytics.
- Understand data access patterns and optimization opportunities.
- Budget alerts.
- Set thresholds and get notified before overruns.
PYTHON
ML & AI workloads
Run machine learning and AI workloads directly in your pipelines. Native Python support within isolated, serverless environments.
- Native Python support.
- Use pandas, scikit-learn, TensorFlow, and any Python library.
- Automated dependencies.
- All your execution dependencies are automatically installed in isolated environments.
- Run in your pipelines.
- Mix and match ML models with your data pipelines.
""" @bruinname: dashboard.ltv_prediction
image: python:3.12
instance: b2.xlarge@bruin """import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from src.utils import fetch_customers_df
df = fetch_customers_df()
# Feature engineering
X = df[['historical_spend', 'tenure_months', 'purchases']]
y = df['lifetime_value']
# Train model
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
model = LinearRegression()
model.fit(X_train, y_train)GOVERNANCE
Catalog, lineage, and control
Automatic lineage generation across platforms. Data catalog with glossary and metadata. Track dependencies from source to dashboard.
Lineage
End-to-end visibility
Track data flow from source systems through transformations to final dashboards. Understand impact before making changes, on a column level.
Catalog
Central metadata registry
Document tables, columns, and pipelines. Business glossary with ownership and descriptions for every asset.
Impact
Identify risks
Automated impact analysis before changes. Know what breaks before deployment.
Control
Role-based access
Fine-grained permissions. Control who can view, edit, and execute.
Multi-repo
Distributed workflows
Connect pipelines across multiple repositories. Works with GitHub, GitLab, Bitbucket and more.
PLATFORMS
Bring your own cloud
You can use our fully managed cloud, or bring your own cloud if you'd prefer. Run in your VPC or on-premises.
Fully managed
Private VPC
On‑premises
Ready to ship reliable data?
Production-ready pipelines without the complexity. Deploy today.