What data warehouses does dbt work with?
dbt supports multiple data platforms, including Snowflake, BigQuery, Databricks, and Amazon Redshift. The right setup depends on your existing data infrastructure and transformation requirements.
dbt Consulting and Implementation Services
Your warehouse holds the data. dbt makes it reliable and ready to use.
Decision Foundry designs and builds dbt projects that replace fragile SQL scripts with tested, documented, version-controlled dbt data models, helping analysts deliver trustworthy insights faster.
The Data Problems dbt Is Built to Solve
Most teams don't struggle with getting data into the warehouse. They struggle with what happens next.
- 01Business logic is scattered across SQL scripts and BI tools, leaving teams without a single source of truth.
- 02Data models lack automated tests, so errors surface in executive reports instead of triggering alerts.
- 03Poor documentation makes it difficult for new engineers to understand tables, dependencies, and transformation logic.
- 04Analysts recreate the same business logic across dashboards, leading to conflicting metrics and unreliable reports.
- 05Without version control even small changes can break downstream models and reports.
- 06Poorly structured dbt projects, inconsistent naming conventions, and inefficient models make transformations harder to maintain and scale.
These problems are solvable. dbt brings structure, testing, documentation, and version control to data transformations, but it takes the right implementation to make them work together.
Our Expertise: Six Ways We Implement dbt
dbt Project Architecture & Setup
We establish a scalable dbt foundation with a clear project structure, consistent naming conventions, and separate development and production environments. We can help with dbt data modeling using dbt Cloud or dbt Core and set up orchestration to support reliable, maintainable workflows.
Data Modeling & Transformation
We translate business rules into modular, reusable SQL and Python models. From dimensional modeling to incremental processing and historical snapshots, we structure transformations to improve data consistency, simplify maintenance, and support reporting needs.
Testing, Data Quality & Observability
We integrate automated tests, source freshness checks, and data contracts to identify issues before they reach dashboards or downstream systems. This helps teams detect unexpected changes, maintain data quality, and build confidence in reporting.
Automated Testing & Deployment
We introduce Git-based workflows, pull request reviews, and automated testing to make changes safer and easier to manage. With selective checks and automated deployments, teams can release updates consistently while reducing the risk of breaking dependent models.
Semantic Layer & AI Readiness
We help define business metrics consistently through dbt's Semantic Layer and MetricFlow. By documenting models and standardizing metric definitions for BI tools and AI applications, we make trusted business data easier to access and use.
Migration, Optimization & Rescue
We migrate legacy SQL transformations, stored procedures, and ETL workflows to dbt while preserving essential business logic. For existing projects, we improve model structures, optimize query performance, and streamline execution to reduce warehouse costs and maintenance overhead.
Ready to Implement dbt?
Build a scalable dbt foundation that simplifies data transformations, improves data quality, and brings consistency to your analytics.
dbt Core vs. dbt Cloud
Choosing between dbt Core and dbt Cloud depends on how your team develops, runs, and manages data transformations. Both support dbt workflows, but they differ in how much infrastructure and operational management your team handles.
| Feature | dbt Core | dbt Cloud |
|---|---|---|
| Deployment | Open-source command-line tool | Managed dbt platform |
| Development | Develop and run models through the command line | Integrated development environment and workflows |
| Infrastructure | Your team manages the supporting setup | Provides managed infrastructure and services |
| Scheduling | Requires external tools or custom configuration | Built-in job scheduling |
| Testing & Deployment | Requires separate tools and configuration | Integrated capabilities for testing and deploying changes |
| Monitoring | Requires additional tools or custom setup | Built-in monitoring and alerting capabilities |
| Best suited for | Teams that want flexibility and control over their setup | Teams that prefer integrated development and operational workflows |
We assess your data stack, team capabilities, and operational needs to help you choose and implement the dbt data models that fits your organization.
Our dbt Consulting Process
- 1
Assess Your Data Environment
We review your existing SQL transformations, data sources, pipelines, and reporting requirements to identify gaps and define implementation priorities for dbt modeling.
- 2
Plan the Implementation
We establish the dbt project structure, modeling approach, testing requirements, and deployment strategy based on your team's needs.
- 3
Build and Transform
We develop modular dbt models, organize transformation logic, and add tests and documentation to create maintainable data workflows.
- 4
Validate and Deploy
We verify transformed data against existing outputs, resolve discrepancies, and configure deployment workflows to support reliable production releases.
- 5
Enable and Optimize
We help your team manage the dbt project, improve model performance, control warehouse costs, and expand the implementation as your needs evolve.
Where dbt Sits in Your Data Stack?
Data sources
Where raw data starts
- CRM, ERP
- Ad platforms
- Web apps
Ingestion
Extract and load data
- Fivetran
- Airbyte
- Supermetrics
Warehouse
Store and compute
- Snowflake
- Databricks
- BigQuery
dbt transform
Model, test, document
- Clean data
- Combine data
- Ready to use
Semantic layer
Define each metric once
- dbt Semantic Layer
- MetricFlow
BI and AI
Dashboards, activation
- Tableau
- Power BI
- Data Cloud
- AI agents
Why Choose Decision Foundry for dbt Consulting?
20+ Years of Data Expertise
With 20+ years of enterprise data experience, 1,000+ projects delivered, and 600+ customers, we bring proven expertise in solving complex data challenges.
Tailored to Your Business
We design dbt implementations around your existing tech stack, business goals, data maturity, and team requirements, not a one-size-fits-all approach.
Connected Across Your Tech Stack
As a Snowflake Select, Databricks, Fivetran, and Salesforce Select Partner, we connect ingestion, transformation, BI, and AI to turn trusted dbt models into business outcomes.
Certified and Secure
Our teams hold 25+ vendor-issued certifications across data, cloud, and security technologies. We align our delivery practices with relevant requirements, including SOC 2, HIPAA, and GDPR.
Looking for the Right Partner for dbt Implementation?
Decision Foundry brings expertise to help you design, implement, and optimize dbt around your business needs. From connecting your existing data stack to building reliable, scalable transformation workflows, we help turn complex data into a trusted foundation for analytics and AI.
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Frequently Asked Questions
Can dbt replace our existing ETL tools?
dbt can replace or simplify the transformation layer of existing ETL workflows. Data ingestion and extraction tools may still be needed to move data into your warehouse.
Should we choose dbt Core or dbt Cloud?
dbt Core offers an open-source command-line approach, while dbt Cloud provides a managed environment with additional development and orchestration capabilities. The choice depends on your team's technical expertise, workflow, and operational needs.
Can dbt handle large and complex data environments?
Yes. With appropriate model design, incremental processing, and warehouse optimization, dbt can support complex transformation workflows across large datasets.
Who should manage dbt after implementation?
Data engineers typically manage models, testing, and deployments, while analytics engineers and analysts contribute to business logic and reporting models. Clear ownership and documentation help teams maintain the project.
How do we measure the success of a dbt implementation?
Success can be measured through improved data quality, fewer reporting discrepancies, faster transformation runs, reduced maintenance effort, and greater consistency in business metrics.