To understand what a data warehouse is, we will rely heavily on the following metaphor: let’s imagine a data warehouse as a library that stores a massive quantity of books.
There, librarians can fetch the books and answer questions about them. Often, quickly. Sometimes, not so much.
Now that we have set the stage, let’s go for a trip down memory lane...
In the early 2000s, "Big Data" was booming.
To keep up with the increasing load and demands, systems needed to evolve and improve. The problem was that the majority of new solutions were built on existing tech.
This meant they were constrained by outdated limitations, often requiring significant upfront investments and hard-to-find specialized talent.
(Some readers might remember dealing with systems like Teradata, Netezza, Vertica etc…)
The creators of Snowflake opted for a different approach: they built their product from scratch, letting go of the old and focusing on key features that were rare to find in a single solution.
