I’m not going to cover the setup of a project - Google has done a great job of this for us: You will need to set one up before you can deploy instances or use BigQuery. You could, of course, deploy all of this programmatically, but we’ll keep it simple for now.Ī “project” is the top-level container for resources you deploy in GCP - you might be more used to hearing these called accounts or subscriptions in AWS and Azure respectively. Oh, and the best bit? Because this is all done on the cloud, you can follow this guide from login to query in less than 30 minutes using the web-based console. You might use this guide to run your own proof-of-concept or to perform ad-hoc data analysis for projects or assignments. I’ve put this short guide together to show a clear example of just how easy it is to provision an RStudio instance on GCP and use that instance to access the scalable power of BigQuery to perform complex analytics. Whilst there are strengths and weaknesses in all of the tools, one of the challenges I’ve become aware of is accessing scalable compute from within RStudio, a common IDE used by data scientists to produce analytics using the R and Python languages. I’ve recently had a chance to play with some of the newer tech stacks being used for Big Data and ML/AI across the major cloud platforms.
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