Data architecture can be difficult to understand – when new to the subject, it can be very hard to distinguish between hype and concept, between real things (with obscure names) and conceptual names which are actually marketingy gizmos. Over time in consulting I come back to a fish based explanation, which I synthesised in a phone call last year with someone and have finally committed to paper.
My firm have recently signed up with Snowflake, so on a spare afternoon I decided to compare the different ways you can connect data to Snowflake from Alteryx. I’m doing this with some Kickstarter data I found on data.world; I’m breaking the data down into four tables, and writing each table with a different method.
You’re not really meant to say this when you are (or have been) a data analyst/scientist/whatever, but I have a limited patience/tolerance for the reformatting and cleaning of data.