dbt Reviews

Summary: dbt Cloud is the "iPhone" of data transformation: The undisputed standard for SQL transformation, balancing a powerful "zero-setup" ecosystem against a complex consumption-based pricing model. It is the best choice for teams that want to move fast and minimize DevOps overhead.

Positive: Ease of use and Features. Easy to setup, integrate, and get started quickly
Less maintenance
Out of the box CI/CD integration with Git
Easy to learn.

Negative: Limited product Usage metrics. Product usage insights/Metrics can be better.
Metrics around AI usage by developers with in the product will help.

Summary: dbt platform hits a sweet spot between offering a broad set of features and requiring minimal system administration overhead

Positive: - Credential and version management is offloaded to the cloud
- Simple-to-use orchestration
- Seamless state management
- Integrated documentation and lineage
- Collaborative development experience
- Native CI/CD integration
- Centralized logging and observability
- Enterprise-grade access control and auditability
- Easy environment management
- Rapid onboarding for new users

Negative: - Individual capabilities are not as robust as dedicated tools. for example, orchestration is simple to use but lacks the flexibility, customization, and advanced scheduling logic of dedicated orchestrators

Summary: In general my experience is great. I really like using dbt and it's a simple tool to set up that offers a lot of benefits. The Cloud IDE and platform is really helpful and we can onboard analysts at a much faster rate than before. It is very useful and helpful for both technical and not technical users.

Positive: We use dbt for our data transformations. It's been a game changer from a Data Engineering and Analytics Engineering standpoint. It has accelerated our migration from legacy systems and made our pipelines 80% faster. We have increased visibility in our projects, a catalog and many other data quality indicators.

Negative: I think that the pricing model can easily become a barrier. The cost per model run is a terrible bottleneck for us and affects our capacity to architect following best practices.

Summary: dbt is the most impactful tool I’ve adopted for building scalable, governed analytics. It’s dramatically improved our velocity, reliability, and the clarity of our data pipelines. By enforcing tests, version control, and modularity, dbt makes it much harder for silent data debt to accumulate. Having to test and document every model cultivates a mindset of rigor that carries over into the rest of the data lifecycle. It basically pushes teams toward cleaner patterns and long-term maintainability. I also love that, because branching, CI, and partial runs are built in, dbt makes experimentation with new metrics, features, and data products safer and faster — you can prototype without risking production quality.

Positive: dbt has been one of the most transformative tools in my data career. It gives teams a clean, maintainable way to translate business logic into reliable, production-grade data models. It standardizes the entire development lifecycle — modeling, testing, documentation, version control, CI/CD, and lineage — in a way that allows analytics engineers and data engineers to work with clarity and confidence. It’s the backbone of our governed analytics strategy.

Exceptional developer workflow: Modular SQL, version control, built-in testing, documentation, and macros allow us to scale complex business logic with consistency and reliability.

Scales with organizational change: dbt has allowed us to redesign core product and customer analytics with patterns that are resilient to future product launches and schema changes.

Negative: dbt IDE could be more flexible with Git operations.
Advanced users would benefit from features like git stash, more granular branch management, and better conflict-resolution tools directly in the IDE. This would remove friction during rapid iteration or when working across multiple branches.

More built-in patterns for complex incremental modeling would be helpful for teams dealing with very high data volumes and dynamic product schemas.