dbt GREAT for Most Data Orgs
May 20, 2025

dbt GREAT for Most Data Orgs

Sid Iyer | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Software Version

dbt Enterprise

Overall Satisfaction with dbt

We used dbt to transform source data into data tables, push these data tables into our data warehouse, establish sources of truth for data, track data lineage / dependencies / downstream impacts, and as a source of truth for business logic (metric definitions, what data can be used for what, and so forth).

Pros

  • Easy to create data tables (analysts can do what used to require engineer)
  • Documentation & lineage is built in
  • It can run tests to make sure data is being transformed correctly

Cons

  • You have to use a separate tool for job orchestration
  • You need a separate tool for ingestion
  • Table optimization work is manual (no automations there)
  • Analytics team members can now do what required more expensive engineers
  • Can save on other tools for lineage, unit tests, etc.
dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.
dbt is very flexible and can fit into most data pipelines. This is a pro for most organizations that aren't fully bought into one platform (Google Cloud, etc.)

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

dbt is really good for certain more mature data organizations that are primarily doing SQL based analytics. If your organization is younger (you don't haver very many data tables you need to manager), dbt is probably overkill. If the ways you use data are more complex (e.g. primarily data science using python) then dbt may not be the most efficient platform for your workflow.

dbt Feature Ratings

Data Source Connection ()
Not Rated
10.0
Simple transformations
10
Complex transformations
10
9.2
Data model creation
10
Metadata management
9
Business rules and workflow
9
Collaboration
10
Testing and debugging
8
Data Governance ()
Not Rated

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