when implemented with efficiency and care, dbt helps creating reusable and accessible data models
Updated May 26, 2022

when implemented with efficiency and care, dbt helps creating reusable and accessible data models

Obed Espina | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Software Version

dbt Enterprise

Overall Satisfaction with dbt (data build tool)

I'll quickly summarize one pain point. We have data transformation jobs (SQL-only) written in Airflow, and often an analyst teammate had most of the business context. However, there is a higher barrier to entry to jump into Airflow-based development, so data engineering was becoming the bottleneck to data model changes. By introducing dbt (data build tool) along with support from data engineering, we were able to open up data modeling to other teams without having to wait for Airflow changes. This helps because these teams have the business context for that data model and are best equipped to make those changes. There is more detail at this public blog post: https://medium.com/vimeo-engineering-blog/dbt-development-at-vimeo-fe1ad9eb212
  • user experience makes it easy to work with SQL and version control
  • customer success team and the dbt (data build tool) community help establish best practices
  • thorough and clear documentation
  • increased customization for incremental models to support larger data sets
  • suggestions for project structure to fit legacy models (e.g. a legacy table built by another ETL)
  • browser-based UI to simplify development
  • configurable and cloud-based Jobs
  • dbt's flexibility with fitting to the our organizations' data standards
  • increased data model commits by non-data engineering teams
  • clearer project and data model structures guided by business domains
Airflow can accomplish the same work as dbt (data build tool), however, dbt's (data build tool) development workflow and UI can open up data transformation and modeling work to non-data engineering teams. Looker might also be able to define data models via LookML with a version control-like workflow. However, there's an added step here to learn LookML, compared to dbt (data build tool) which primarily relies on SQL (note: there is also Jinja so you could argue that you need to learn this too).

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dbt (data build tool) has the capability to make your data models more accessible; other teams can read documentation, follow along the lineage, and even collaborate to make changes themselves dbt (data build tool) also has the capability to easily increase your database cost and write complex data models. The key to mitigating this risk is to adhere to best practices from the community and within your organization. Look to your data engineering teams to help guide scalable and efficient dbt (data build tool) processes and listen to your analysts for building well-documented and reusable data models.

dbt Feature Ratings

Simple transformations
Complex transformations
Data model creation
Metadata management
Business rules and workflow
Testing and debugging