Alooma vs. dbt

Very appropriate for managing an ELT stack -- Alooma handles the 'E' and 'L' pieces very well. Due to the way transformation is setup, it is more on the lightweight side of things and thus works for this kind of stack. For more traditional ETL stacks, Alooma would probably not be the right fit.

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

• Transformation of data before entering the data warehouse -- allows for a consistent/understandable schema for analytics teams.
• Adding of additional data sources: Alooma has a large library of pre-built, common 3rd party vendors for which data is often needed to be ETL-ed.

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

• More tools on managing the data warehouse itself. Although great on the ingest and transformation of data, more help provided by the tool on managing the outputs would be welcome.
• Increase possibilities in the transformation step, rather than single event transformations.

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

Haven't used the alternatives, since Alooma was one of the earlier ones to the market. From a cursory viewing, however, the features seem to match relatively similarly.

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

• Allowing for additional data sources to more automation capability around monitoring the business is a huge ROI

Incentivized

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

Incentivized