Manage your data transformations with engineering practices.
January 08, 2025

Manage your data transformations with engineering practices.

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Software Version

dbt Enterprise

Overall Satisfaction with dbt

We use dbt to manage all the data transformation logic in our data warehouse, all the way from raw data to modeled data ready for analysis. This allows us to harmonize and clean our data and create models combining data from multiple sources. Our scope contains billing and payment data, CRM data, marketing, and lead pipeline data, etc.

Pros

  • Automation
  • Version control.
  • Automated generation of lineage graphs.

Cons

  • Tried hard, but cannot think of anything.
  • Ability to analyze marketing campaign effectiveness.
  • Ability to calculate a customer health score used to reduce churn.
  • Ability to created trusted financial reporting, e.g. billings and recurring revenue.
It requires proficiency with SQL coding and with git practices, but with these prerequisites, it is easy to use. Especially with the dbt cloud, you get a nice interface that makes all the administrative tasks like scheduling jobs quite easy. I also like the built-in SQL editor with syntax highlighting and auto-completion.
Matillion is graphical versus dbt, which is SQL code-based (that, of course, is a matter of personal preference and not an objective advantage). The integrated testing, documentation generation, lineage, etc., were additional criteria that led us to choose dbt.

Do you think dbt delivers good value for the price?

Yes

Are you happy with dbt's feature set?

Yes

Did dbt live up to sales and marketing promises?

Yes

Did implementation of dbt go as expected?

Yes

Would you buy dbt again?

Yes

The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.

dbt Feature Ratings

Data Source Connection ()
Not Rated
10.0
Simple transformations
10
Complex transformations
10
8.8
Data model creation
10
Metadata management
8
Business rules and workflow
8
Collaboration
10
Testing and debugging
8
Data Governance ()
Not Rated

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