dbt vs Bruin: Why End-to-End Wins Over Transformation-Only

If you're building data pipelines in 2025, you've likely heard of dbt (data build tool). It's become the de facto standard for SQL transformations in the modern data stack. But here's the problem: dbt only solves one piece of the puzzle.

What if you could get ingestion, transformation, quality checks, and orchestration—all in one tool? That's exactly what Bruin delivers. Let's dive into why an end-to-end approach beats cobbling together multiple tools.

The Fundamental Difference: Transformation vs. End-to-End

dbt: The "T" in ELT

dbt focuses exclusively on transformation. It takes data that's already in your warehouse and transforms it using SQL (and limited Python). That's it. Everything else—getting data into your warehouse, scheduling pipelines, monitoring quality—requires additional tools.

A typical dbt stack looks like this:

Ingestion tool (Fivetran, Airbyte, or custom scripts)
dbt for transformations
Orchestrator (Airflow, Dagster, Prefect, or dbt Cloud)
Observability tools (Monte Carlo, dbt Cloud)
Catalog/Lineage tools (separate or dbt Cloud)

That's 3-5 different tools to manage, configure, and integrate. Each one requires its own authentication, monitoring, and maintenance.

Bruin: Complete Data Pipelines, One Tool

Bruin takes a different approach: everything you need in a single, unified framework.

With Bruin, you get:

✅ Data ingestion (100+ connectors via ingestr)
✅ SQL & Python transformations (both first-class citizens)
✅ Built-in orchestration (no Airflow needed)
✅ Data quality checks (blocking by default)
✅ Column-level lineage (built-in, even locally)
✅ VS Code extension (visual lineage and execution)

One tool. One CLI. One configuration format. One deployment.

Data Ingestion: Built-In vs. Bring Your Own

dbt: No Ingestion Capabilities

dbt doesn't handle data ingestion. You need to solve this yourself with:

Fivetran - Expensive SaaS solution ($$$$)
Airbyte - Open-source but requires deployment and maintenance
Custom Python scripts - Flexible but high maintenance burden
Cloud-native tools - AWS Glue, Azure Data Factory (vendor lock-in)

Each solution comes with its own complexity: separate infrastructure, different authentication systems, and coordination headaches between tools.

Bruin: 100+ Connectors Out of the Box

Bruin's ingestion engine is built on ingestr, an open-source data ingestion tool with 100+ connectors. Define your ingestion with simple YAML:

name: raw.users
type: ingestr
parameters:
  source_connection: postgresql
  source_table: 'public.users'
  destination: bigquery
  incremental_strategy: merge
  incremental_key: updated_at
  primary_key: id

That's it. No separate infrastructure. No complex configuration. Just YAML.

Incremental Loading Strategies

Bruin supports multiple incremental loading strategies out of the box:

1. Append - Add new rows without touching existing data

incremental_strategy: append
incremental_key: created_at

2. Merge - Upsert based on primary keys (updates existing, inserts new)

incremental_strategy: merge
primary_key: id
incremental_key: updated_at

3. Delete+Insert - Delete matching rows, insert new ones

incremental_strategy: delete+insert
incremental_key: date

4. Replace - Full table replacement

incremental_strategy: replace

These strategies ensure you're only moving the data you need, not re-ingesting entire tables every time.

Bonus: Need a custom connector? Bruin offers a 1-week SLA for custom connector development.

Python Execution: First-Class vs. Afterthought

This is where the differences become stark.

dbt: Limited Python Support

dbt added Python support (dbt-py) later in its lifecycle, and it shows:

❌ Platform-specific - Requires DataFrame API specific to your data warehouse
❌ Not all platforms support it - Check if your warehouse even supports Python models
❌ Can't easily mix SQL and Python - Awkward workflow when you need both
❌ Limited flexibility - Constrained by what your warehouse supports

If you want to use scikit-learn, TensorFlow, or any ML library, you're often out of luck with dbt.

Bruin: Python as a First-Class Citizen

Bruin was built from day one with native Python support:

✅ Use any Python library - pandas, numpy, scikit-learn, TensorFlow, PyTorch, whatever you need
✅ Isolated environments with uv - Each asset runs in its own environment
✅ Mix SQL & Python freely - Build pipelines that flow from SQL to Python and back
✅ Cross-language dependencies - SQL assets can depend on Python assets and vice versa
✅ Multiple Python versions - Run different Python versions in the same pipeline

Here's a real example:

# assets/ml_predictions.py
"""
@bruin
name: analytics.ml_predictions
type: python
depends:
  - analytics.user_features  # SQL asset
materialization:
  type: table
@bruin
"""

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

def main(connection):
    # Load data from the SQL upstream dependency
    df = connection.read_sql("SELECT * FROM analytics.user_features")

    # Train your model
    model = RandomForestClassifier()
    # ... model training logic ...

    # Return predictions as DataFrame
    return predictions_df

This Python asset depends on a SQL asset (analytics.user_features), trains a machine learning model, and outputs results that downstream SQL assets can use. This workflow is nearly impossible in dbt.

Isolated Environments Mean No Dependency Hell

Bruin uses uv to manage isolated Python environments for each asset. This means:

No global dependency conflicts
Reproducible execution
Automatic dependency installation
Different Python versions in the same pipeline

# asset.yml
name: analytics.ml_model
type: python
parameters:
  python_version: "3.11"
  dependencies:
    - pandas==2.0.0
    - scikit-learn==1.3.0
    - tensorflow==2.14.0

Jinja Templating: Both Support It

Good news if you're migrating from dbt: Bruin supports Jinja templating too.

-- assets/daily_revenue.sql
/*
@bruin
name: analytics.daily_revenue
depends:
  - raw.orders
@bruin
*/

SELECT
  DATE_TRUNC('day', created_at) as date,
  SUM(amount) as revenue
FROM raw.orders
WHERE created_at >= '{{ start_date }}'
  AND status = 'completed'
GROUP BY 1

Bruin supports Jinja templating with:

Variables and parameters
Macros for code reuse
Control structures (if/else, loops)
Custom filters

You're not losing familiar patterns—you're gaining capabilities.

Orchestration: Built-In vs. DIY

dbt: Requires External Orchestration

dbt has no orchestration capabilities. You must use an external tool:

Apache Airflow

Complex setup and maintenance
Steep learning curve
Requires dedicated infrastructure

Dagster

Modern but still requires separate deployment
Another tool to learn and manage

Prefect

Cloud-first approach
Additional service to maintain

dbt Cloud

Managed orchestration option
$$$ - Can get expensive
Vendor lock-in considerations

Each option means more infrastructure, more authentication to manage, and more tools to integrate.

Bruin: Zero Orchestration Overhead

Bruin has built-in orchestration. No Airflow. No Kubernetes. No complexity.

# Run your entire pipeline locally
bruin run

# Backfill historical data
bruin run --start-date 2024-01-01 --end-date 2024-12-31

Deployment options:

Local Development - bruin run on your machine
GitHub Actions - CI/CD integration with single binary
EC2 / VM - Self-hosted, no dependencies required
Bruin Cloud - Fully managed with governance and monitoring

The single binary approach means you can deploy anywhere—no Docker, no Kubernetes, no complex orchestration infrastructure.

Data Quality: Gates vs. Tests

dbt: Tests Run Separately

In dbt, tests are defined separately from transformations:

# schema.yml
models:
  - name: users
    columns:
      - name: email
        tests:
          - not_null
          - unique

Limitations:

Tests don't run automatically—you must execute dbt test
Not part of the pipeline execution flow
Failures don't prevent downstream models from running
Separate mindset: "build models, then test"

Bruin: Quality Checks as First-Class Citizens

In Bruin, quality checks are embedded in your asset definitions and run automatically after each transformation:

# assets/users.sql
name: analytics.users
type: sql
materialization:
  type: table
columns:
  - name: email
    type: string
    checks:
      - name: not_null
      - name: unique
  - name: revenue
    type: float
    checks:
      - name: positive
  - name: country_code
    type: string
    checks:
      - name: accepted_values
        value: ['US', 'UK', 'CA', 'DE', 'FR']

Key advantages:

✅ Blocking by default - Bad data can't proceed to downstream assets
✅ Automatic execution - Runs after every transformation
✅ Works on all asset types - Ingestion, SQL, Python
✅ Custom SQL checks - Define any validation logic you need
✅ Optional non-blocking mode - For monitoring without stopping pipelines

Custom checks example:

custom_checks:
  - name: revenue_matches_sum
    query: |
      SELECT COUNT(*) FROM analytics.users
      WHERE total_revenue != (
        SELECT SUM(order_amount)
        FROM analytics.orders
        WHERE user_id = users.id
      )
    value: 0

If this check fails, the pipeline stops. No bad data flows downstream.

Performance: Go vs. Python, Single Binary vs. Stack

dbt Performance Challenges

As dbt projects grow, teams encounter:

❌ Scalability issues - Projects with 400+ models become slow
❌ Long compilation times - Jinja templates add overhead
❌ DAG complexity - Dependency resolution slows down
❌ Python overhead - dbt is written in Python, which isn't the fastest
❌ Fusion engine not open-source - dbt's performance improvements via the Fusion engine are only available in dbt Cloud (paid), creating a revenue play that pushes teams toward the managed service

Bruin: Built for Performance

Bruin is written in Go, which provides:

✅ Fast execution - Compiled to native machine code
✅ Low overhead - Single binary, no runtime dependencies
✅ Efficient orchestration - Built-in scheduler with minimal overhead
✅ Quick compilation - Processes large pipelines rapidly

Real-World Results: Buluttan Case Study

Weather intelligence company Buluttan migrated from dbt to Bruin and achieved:

3x faster pipeline execution
90% faster deployments
15 minutes to respond to issues (vs. hours before)

"Bruin's product has effectively addressed all the challenges my team faced in developing, orchestrating, and monitoring our pipelines." — Arsalan Noorafkan, Team Lead Data Engineering at Buluttan

These aren't theoretical improvements—they're production results from a real engineering team.

Developer Experience: Tooling That Works

dbt

✅ CLI for running models and tests
✅ Auto-generated documentation
✅ Large community (100k+ users)
⚠️ Steep learning curve with Jinja
⚠️ Limited IDE support beyond dbt Cloud IDE
❌ No lineage visualization in open-source

Bruin

✅ VS Code extension with visual lineage, docs, and execution
✅ Powerful CLI with validation, dry-run, and backfills
✅ Single binary installation - No dependencies, no virtual environments
✅ Built-in lineage visualization - Even locally
✅ Fast feedback loop - Instant validation
✅ Simpler syntax - Less boilerplate, easier to learn

The VS Code extension is a game-changer. You can:

Visualize your entire pipeline graph
See column-level lineage
Execute individual assets or entire pipelines
View documentation inline
Validate configurations in real-time

Architecture Comparison: 5 Tools vs. 1

The dbt Stack

┌─────────────────┐
│ Ingestion Tool  │ (Fivetran/Airbyte)
└────────┬────────┘
         │
┌────────▼────────┐
│      dbt        │ (Transformations only)
└────────┬────────┘
         │
┌────────▼────────┐
│  Orchestrator   │ (Airflow/Dagster)
└────────┬────────┘
         │
┌────────▼────────┐
│ Observability   │ (Monte Carlo/dbt Cloud)
└────────┬────────┘
         │
┌────────▼────────┐
│ Lineage/Catalog │ (Separate tool or dbt Cloud)
└─────────────────┘

Result: 3-5 tools to manage, configure, integrate, and maintain. Different authentication systems. Multiple points of failure. Complex deployment pipelines.

The Bruin Stack

┌─────────────────────────────┐
│          Bruin CLI          │
│                             │
│  • Ingestion (100+ sources) │
│  • SQL Transformations      │
│  • Python Execution         │
│  • Quality Checks           │
│  • Orchestration            │
│  • Lineage                  │
│  • Observability            │
└─────────────────────────────┘

Result: Single tool. One configuration format. One CLI. One deployment. Everything works together out of the box.

When to Choose dbt

To be fair, there are scenarios where dbt makes sense:

✅ You only need transformations - Already have ingestion and orchestration figured out with other tools
✅ Your team is SQL-only - No Python requirements whatsoever
✅ You want dbt Cloud - Willing to pay for the managed experience
✅ You're heavily invested - Already have hundreds of dbt models and migration would be significant effort

When to Choose Bruin

Choose Bruin if you want:

✅ End-to-end solution - Ingestion, transformation, and quality in one tool
✅ SQL + Python - Building ML models, complex analytics, or need flexibility
✅ To move fast - Focus on business logic, not infrastructure
✅ Better performance - 3x faster execution, proven by real teams
✅ Starting fresh or scaling - Building new pipelines or dbt is getting slow
✅ Simpler operations - Single binary, no Airflow, minimal complexity

Migration from dbt: Easier Than You Think

Worried about switching? The migration path is straightforward:

Jinja support - Your SQL transforms work with minimal changes
Clear dependency syntax - Asset dependencies are explicit and easy to understand
Incremental models - Same concept, same patterns
Better performance - Teams report 3x speedups

Plus, you gain capabilities:

Native Python support
Built-in data ingestion
No more Airflow maintenance
Integrated quality checks
Built-in lineage

Conclusion: End-to-End Beats Piecemeal

The modern data stack promised modularity—pick the best tool for each job. In practice, it delivered complexity.

dbt is excellent at what it does, but what it does is limited: SQL transformations. Everything else requires additional tools, infrastructure, and integration work.

Bruin delivers the full pipeline: ingestion, transformation (SQL and Python), quality checks, and orchestration—all in a single, fast, unified tool.

If you're starting a new data project, or if your dbt stack is getting unwieldy, give Bruin a try. It's:

Open source (MIT licensed)
Free to use
Production-ready
Fast (written in Go)
Simple (single binary, no dependencies)

The data pipelines of the future won't be held together by duct tape and five different tools. They'll be unified, fast, and simple.

Get Started

Ready to try Bruin?

Install the CLI:

curl -LsSf https://getbruin.com/install/cli | sh

Try the quickstart:

bruin init my-pipeline
cd my-pipeline
bruin run

Resources:

📘 Documentation
💻 GitHub Repository
🎯 VS Code Extension
💬 Community Slack
☁️ Bruin Cloud (Managed platform)

Did you migrate from dbt to Bruin? We'd love to hear your story. Reach out on Slack or GitHub.