dbt vs. SAP PowerDesigner

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

SAP PowerDesigner allows our team of data modelers to work and collaborate from a single repository and single data dictionary. This helps enforce consistency as data elements are referenced in other objects. Prior to our use of PD, we might have an element named "ppt" in one table, "participant" in another table and "part" in a third table. By forcing everything to be used from the data dictionary, we avoid these situations because everyone has to go to the dictionary. And we are able to easily do peer reviews on models before they are released because we are collaborating through the use of the repository.

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

• Impact Analysis
• The domain structure helps you to handle different data types to unify.
• Moving between from conceptual model to logical model and from logical model to physical model is so easy.
• It also excels at implementation of database vendor-specific physical options.
• The repository structure helps you to use in multi developer and architect based organizations by the check in and check out mechanism.
• It also has a fully configurable naming convention implementation.
• Coloring is simple with format copying and pasting option.

Incentivized

• 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

• It took some time to figure out how to leverage the power of the repository. Some better examples in the documentation would have been helpful.

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

We did have to reach out to support to learn how to properly utilize the repository feature and share the data model across many developers. Support was able to help us get this set up correctly. The downside was it took us several weeks before we gave up and contacted support. We should have done that earlier. I would say, however, the documentation wasn't clear on how to do this. So support was a great big help!

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

The version of ErWin we had didn't support the repository for document sharing and data dictionary sharing. Our version of PD does so we were able to leverage that and have a central repository that is shared among the team members. That really helped to give us consistency across our databases. PD is easy to use, but getting the repository set up properly was a little tricky.

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

• Standardization of models
• Impact Analysis
• Graphical User Interface
• Ease of deployment

Incentivized