dbt vs. Vertify

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

When we first implemented, there were a lot of fields not set up/syncing properly. But by consulting with Vertify, for a lot of hours, we have been able to work through a lot of those errors, and our Account Manager and the tech support people like Markus and Jamie have been very supportive and helpful in getting us up and running properly.

• 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

• All clients in the CRM are mirrored in the ESP.
• Data points about client interaction are pushed to the email tool.
• Emails can be triggered based off activities that are tracked in the CRM.
• One or two-way syncs can be configured.

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

• I think the UI could get a refresher. It looks a little old-school and I sometimes get lost in the navigation.
• I saw some inconsistencies on the scheduler, most likely caused by time zone differences. One dialogue reflected the user's time zone, the next server's time zone.

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

Creating a mapping between source and target while also using lookups and transformations is not trivial. And VertifyData solved this reasonably well, at least all users in my organization understood it pretty quickly.

It is not the easiest user interface to read/understand. However, once you understand how it works, then using it is not that bad. It's hard to remember what feature is listed under what tab (Manage vs. Define). A suggestion would be to get all call to actions on the same page

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

We were using Scribe for similar tasks but with different connectors. And it served us well, so absolutely no hard feelings here. But when it came to creating complex workflows, we instantly understood Vertify's approach better. Data lookup and transformation e.g. are pretty easy in Vertify and ease of use was definitely what we were looking for.

• 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

• Independently, teams are able to carry out their process with less communication and at a higher level of accuracy.
• VertifyData is actively taking into consideration, consulting, and supporting our plans to build out the business