dbt vs. Vertify

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.

Incentivized

I would recommend it, as VertifyData exactly fit our use case. I can't speak for all use cases and all connectors - naturally - but the ones we are using and have explored so far, work perfectly well. Also, being a person myself that is not fluent in SQL or JSON or API language in general, I was still able to create all workflows our company needed myself. Which I consider a huge benefit.

• dbt supports version control through GIT, this allows teams to collaborate and track the data transformation logic.
• dbt allows us to build data models which helps to break complex transformation logic into simple and smaller logic.
• dbt is completely based on SQL which allows data analyst and data engineers to build the transformation logic.
• dbt can be easily integrated with snowflake.

Incentivized

• Communication.
• Follow up.
• They're constantly trying to improve.

• Field-level lineage (currently at table level)
• Documentation inheritance - if a field is documented the downstream field of the same name could inherit the doc info
• Adding python model support (in beta now)

Incentivized

• Automated error trouble-shooting is not always clear/helpful
• Clearly distinguishing internal errors vs. external errors
• Perhaps a blog where users can interact with questions + solutions

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

I actually don't know what the alternative to dbt is. I'm sure one must exist other than more 'roll your own' options like Apache Airflow, say, bu tin terms of super easy managed/cloud data transforms, dbt really does seem to be THE tool to use. It's $50/month per dev, BUT there's a FREE version for 1 dev seat with no read-only access for anyone else, so you can always start with that and then buy yourself a seat later.

Incentivized

Vertify offered more flexibility and was presented as a simple solution. In reality, it is more complex that we envisioned and we have never fully utilized our tools due to the lack of ability to configure things properly.

Incentivized

• Simplified our BI layer for faster load times
• Increased the quality of data reaching our end users
• Makes complex transformations manageable

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