Adverity vs. dbt

Adverity is particularly useful if there is a large range of data sets that you want to combine to get an 'overall' view. Previously we had used Google Analytics, but found that this was too useful for our big client accounts that we were working on. So I think that if there is an individual who is responsible for analytics or data, and also a paid media team then this is a tool which is essential. For companies that have limited activity then I think this tool could potentially over-complicate for less reward.

Incentivized

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

• User-friendly report setup and ingestion
• Easy integration with multiple data endpoints, making it easy for users to drive value
• Incredible customer support, allowing users (regardless of technical background) to act like power-users

Incentivized

• 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

• I think it would have been more user friendly if there was more labelling capabilities, so that when you are sharing the dashboard they would explain what the data is - rather than depending on someone knowing how to use the dashboards or being there to explain it.
• Our teams found that some of the scheduling functionality could be a little big buggy, so this was something that needed extra care and attention, so therefore may be an area that needs to be improved (unless it was just our account and usage).

Incentivized

• 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

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

Frankly, the service, UI, and ease-of-use for Adverity made it a no brainer compared to competiton.

Incentivized

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

• This tool has allowed us to be able to see areas for opportunities, when all of the data has been combined
• We were also able to better prioritise actions and where to focus our attention, when we had one view where all of the data was together. This allowed us to deliver better results for our clients, and also have a clearer roadmap of activity

Incentivized

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

Incentivized