A tutorial for GCP practitioners starting out with financial data
Introduction
This article will show you one of the ways you can process stock price data using Google Cloud Platform’s BigQuery, and build a simple dashboard on the processed data using Google Data Studio.
Learning to do so can be especially useful for anyone who wishes to automate the findings from stock price insights, and is looking for an efficient and fast way to store the whole process on a cloud platform.
_This article is meant to act as a continuation, or ”part 2", to a previous article in which I showed How to automate financial data collection with Python using APIs and Google Cloud**. _**Feel free to give it a read if you are interested in the upstream data import and script automation side of this workflow. If not, just skip and read on.
Step 1: Identify BigQuery’s Data Sources
GoogleBigQuery is GoogleCloud’s data warehousing solution (one of the many) and quite ideal for working with relational data such as those in this tutorial.
In part 1, I illustrated how you can automate the data feeds into BigQuery using Cloud Functions. In this next step, you are going to be using the same data sources (daily stock price data from S&P500 firms, as well as related mapping tables which will allow us to enrich the data with some categorical variables ) to build a simple & neat processing and data visualization pipeline.
N.B. The following screenshots will be taken from my own GCP Console (which I have set up with Italian as a default language). I have documented each screenshot with explanations so that everyone is able to follow along in English.
To get started, once logged into BigQuery’s editor and, provided you have set up a dataset, you can identify the uploaded data sources by simply clicking on the “Resources” tab on the left side of the editor’s page.
#sql #google-data-studio #google-cloud-platform #bigquery #data analytic