My 0$ “real-world” Data Science environment at home: Database, ETL, Data analytics, Dashboarding, in 9 steps. In this my first Medium post I want to share with you a way to create a “real-world” data environment at home, based on the same components that most companies use to handle their data.
My 0$ “real-world” Data Science environment at home: Database, ETL, Data analytics, Dashboarding, in 9 steps.
In this my first Medium post I want to share with you a way to create a “real-world” data environment at home, based on the same components that most companies use to handle their data. Of course, on a much much smaller scale, and for free.
Why? I used similar frameworks when I was preparing for job interviews, to learn more about new Data Science tasks, reporting, database administration and development, and also to organize my own work.
The framework I’m presenting here is the best I’ve never done so far, in terms of capabilities and time needed to create it (~40 min). Still, it took me a bit of time to figure out how to interface correctly the components, so here I explain directly the clean way to make the whole chain working.
What do data environments in “real-world” companies look like? They usually are a complex and sometimes redundant combination of different tools. But if you look at the core functionalities, most of them can be summarized in just 4 pillars: storing, processing, analyzing, and making decisions from data.
In our context, that translates into the following 4 components
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