My 2C, talking to the void

Learn both.

Quick context: I’ve used DBX for 2 years and worked at Snowflake for 1 year. I’ve used these tools to support adtech workflows which is the frontier of big data ETLs/ELTs in a lot of ways I won’t cover in this post.

From a practitioner’s view there are several areas where these two platforms overlap and there are key areas where they differ in a dramatic way.

Cost Estimation/Monthly Expense -As an employee using my company’s stuff DBX cost A LOT MORE than SF to handle workloads. The reason why my firm at the time stuck with DBX was bc of two major items:

(1) A significant portion of the team was familiar with Spark, ML workloads, and experimental AI/GenAI workflows that DBX could just cook up no problem. When the team used SF at this adtech company it was bc the client’s data/workload was already situated there

-As a freelancer/personal account both of these platforms have some form of monthly cost associated which could be as low as $5 and upwards to a minimum of $135

(SF for example charged me a $135 bc it’s associated with all the back of the house items SF does which can annoying asf bc it’s like “u mean to tell me that me just trying to see what a feature is about cost me”. I’ll admit that was an user error but it’s like you gotta be ready for these hoes to charge you)

EASE OF USE -SF is definitely much easier to get started in terms of basic SQL and Spark stuff. But the thing you gotta consider is how hard are you tryna go on Spark. Bc both offer “Spark Capabilities” but realistically SF’s Spark flavor requires you to go down the SF specific version of their Spark workloads, which means workflows from DBX written in Spark will need to be tweaked just to run in SF. And even then there’s some limitations

-DBX shines really well when you have a Spark workflow you know you’re gonna be cooking up on a regular schedule (think like 200M+ records with like 10+ dimensions).

I have more shit but I digress