dbt vs Bruin
Complete Comparison
An honest, in-depth comparison between dbt and Bruin to help you choose the right data pipeline tool for your team. Compare features, architecture, performance, and use cases.
| Feature | dbt | Bruin |
|---|---|---|
| Data Ingestion | ||
| SQL Transformations | ||
| Python Support | Limited | First-class |
| Built-in Orchestration | ||
| Data Quality Checks | Tests | Built-in |
| Lineage Visualization | Cloud only | Built-in |
| VS Code Extension | ||
| Multi-language Pipelines | ||
| 100+ Data Connectors | ||
| Incremental Loading | ||
| Open Source | ||
| Managed Cloud |
Core Philosophy
Different Approaches to Data Pipelines
dbt (data build tool)
dbt focuses exclusively on the transformation layer of your data stack (the "T" in ELT). It's designed to bring software engineering best practices to analytics workflows.
- SQL-first: Write transformations as SELECT statements
- Cloud-native: Executes in your data warehouse
- Modular design: Break logic into reusable models
- Testing framework: Built-in data testing capabilities
- Large ecosystem: 100k+ community, many packages
Best suited for teams that already have ingestion and orchestration solutions in place and want a dedicated transformation tool.
Bruin
Bruin is an end-to-end data pipeline tool that brings together ingestion, transformation, and data quality into a single, unified framework.
- All-in-one: Ingestion, transformation, and quality in one tool
- Multi-language: Native SQL and Python support
- Built-in orchestration: No Airflow or Dagster needed
- Batteries included: Quality checks, lineage, templating
- Flexible deployment: Local, EC2, GitHub Actions, or managed cloud
Best suited for teams that want a unified solution and prefer to focus on business logic rather than managing infrastructure.
Architecture
How They Work
dbt Architecture
Typical dbt Stack:
- 1.Ingestion tool (Fivetran, Airbyte, custom scripts)
- 2.dbt for transformations
- 3.Orchestrator (Airflow, Dagster, Prefect)
- 4.Observability tools (Monte Carlo, dbt Cloud)
- 5.Catalog/Lineage tools (separate or dbt Cloud)
Result: Multiple tools to manage, integrate, and maintain. Each tool requires its own configuration, monitoring, and potentially separate teams.
Bruin Architecture
Bruin Unified Stack:
- ✓Ingestion (100+ connectors via ingestr)
- ✓SQL & Python transformations
- ✓Built-in orchestration
- ✓Data quality checks
- ✓Column-level lineage
Result: Single unified tool with consistent configuration, one CLI, one deployment process. Everything works together out of the box.
Data Ingestion
Getting Data In
dbt
No Built-in Ingestion
dbt only handles transformations. You need separate tools for data ingestion.
Common Solutions:
- • Fivetran ($$$$ - expensive, SaaS-only)
- • Airbyte (open-source but requires deployment)
- • Custom Python scripts (maintenance burden)
- • Singer taps (limited connectors)
- • Cloud-native tools (AWS Glue, Azure Data Factory)
Challenge: Managing separate ingestion infrastructure, different authentication systems, and ensuring coordination between tools.
Bruin
Built-in Ingestion
100+ connectors via ingestr, defined with simple YAML configuration.
Example: Ingest from PostgreSQL
name: raw.users
type: ingestr
parameters:
source_connection: postgresql
source_table: 'public.users'
destination: bigqueryIncluded Features:
- ✓ Incremental loading out of the box
- ✓ Quality checks on ingested data
- ✓ Unified with transformations in same pipeline
- ✓ 1-week SLA for custom connectors
Transformations
SQL & Python Support
dbt
SQL Transformations
- Excellent SQL support with Jinja templating
- Macros for code reuse
- Materializations (table, view, incremental)
- Jinja can become complex at scale
Python Support (dbt-py)
- Limited Python support (added later)
- Requires DataFrame API (platform-specific)
- Not all platforms support Python models
- Can't easily mix SQL and Python in same workflow
Bruin
SQL Transformations
- Full SQL support with Jinja templating
- Materializations (table, view, incremental)
- Cross-platform compatibility
Python Support
- First-class Python support from day one
- Use any Python library (pandas, sklearn, etc.)
- Mix SQL & Python in the same pipeline
- Isolated environments with uv
- Cross-language dependencies
Perfect for ML/AI: Combine SQL transformations with Python ML models in the same workflow seamlessly.
Performance
Speed & Scalability
Real-World Results: Buluttan Case Study
Weather intelligence company Buluttan migrated from dbt to Bruin and achieved remarkable improvements:
"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
dbt Performance Challenges
Scalability Issues
As projects grow beyond 400+ models, dbt can become slow and difficult to manage. Large DAGs take longer to compile and run.
Dependency Complexity
Managing dependencies between hundreds of models becomes cumbersome. Circular dependencies and incorrect ordering can cause pipeline failures.
Warehouse Compute
All transformations run in the data warehouse, which requires careful optimization to manage compute resources effectively.
Compilation Overhead
dbt compiles Jinja templates before execution, which adds overhead, especially for large projects with many macros.
Bruin Performance Advantages
Written in Go
Bruin is built with Go, providing fast execution and low overhead. Compiles to a single binary with no runtime dependencies.
Optimized Orchestration
Built-in orchestration means no external scheduler overhead. Dependency resolution is efficient even with complex pipelines.
Faster Deployments
Single binary deployment with no external dependencies. Changes can be validated and deployed in minutes, not hours.
Proven Results
Teams report 3x performance improvements over dbt and significantly faster development cycles.
Data Quality
Testing & Validation
dbt Tests
dbt provides a testing framework where you define tests in YAML or SQL. Tests are separate from transformations and need to be explicitly run.
Built-in Tests
- • not_null
- • unique
- • accepted_values
- • relationships (foreign keys)
Custom Tests
Write custom tests as SQL queries that return failing rows.
Limitation: Tests run separately from transformations. Quality checks aren't inherently part of the pipeline execution flow.
Bruin Quality Checks
Quality checks are first-class citizens in Bruin, executed automatically after every transformation. They're blocking by default, preventing bad data from flowing downstream.
Built-in Checks
- not_null, unique, accepted_values, positive, negative, non_negative
- Pattern matching: regex validation
Custom SQL Checks
Define any custom validation logic in SQL with templating support for incremental checks.
Key Advantages
- Blocking by default: Bad data can't proceed
- Works on ingestion, transformation, and any asset type
- Automated alerting on failures
- Optional non-blocking mode for monitoring
Orchestration & Deployment
How You Run Pipelines
dbt
No Built-in Orchestration
dbt needs an external orchestrator to schedule and manage pipeline runs.
Common Orchestrators:
Apache Airflow
Complex setup, steep learning curve, maintenance burden
Dagster
Modern but requires separate infrastructure
Prefect
Cloud-first, additional service to manage
dbt Cloud
Managed option with vendor lock-in considerations
Challenge: Setting up and maintaining orchestration infrastructure, learning another tool, managing authentication between systems.
Bruin
Built-in Orchestration
No external orchestrator needed. Bruin manages scheduling and execution natively.
Deployment Options:
Local Development
Run pipelines locally with bruin run
GitHub Actions
CI/CD integration with single binary
EC2 / VM
Self-hosted, no dependencies required
Bruin Cloud
Fully managed with governance, lineage, and monitoring
Advantage: Zero orchestration overhead. Deploy anywhere with a single binary. No Airflow, no Kubernetes, no complexity.
Developer Experience
Tools & Workflow
dbt
- CLI for running models and tests
- Auto-generated documentation
- Large community with packages and examples
- 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
- Simple installation: single binary, no dependencies
- Built-in lineage visualization (even locally)
- Fast feedback loop with instant validation
- Easy to learn: simpler syntax, less boilerplate
Decision Guide
When to Choose Each Tool
Choose dbt if...
You only need transformations
Already have ingestion and orchestration figured out with other tools.
Your team is SQL-only
You work exclusively with SQL and don't need Python capabilities.
You want dbt Cloud
Willing to pay for dbt Cloud's managed experience and are okay with the ecosystem.
You're heavily invested
Already have hundreds of dbt models and migration would require significant effort.
Choose Bruin if...
You want an end-to-end solution
Need ingestion, transformation, and quality in one unified tool.
You need SQL + Python
Building ML models, complex analytics, or need the flexibility of both languages.
You want to move fast
Don't want to spend time configuring and maintaining multiple tools. Focus on business logic.
You're starting fresh or scaling
Building new pipelines or your dbt setup is getting slow and complex.
You want better performance
Looking for 3x faster execution and simpler infrastructure (proven by real teams).
Ready to ship reliable data?
Production-ready pipelines without the complexity. Deploy today.