dbt vs. Upsolver

dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.

Incentivized

• 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

• Slow load times of the dbt cloud environment (they're working on it via a new UI though)
• More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros

Incentivized

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.

Incentivized

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.

Incentivized

• In 3 months we re-wrote the data warehouse (15-20 sources) in dbt with 3 developers.
• We are using it continually for the past year with no issues.
• Sorry, I don't have ROI numbers but the impact was huge.

Incentivized