How to build a modern data platform on the free tier of Google Cloud Platform

I released a series of seven free public articles on Medium.com “How to build a modern data platform on the free tier of Google Cloud Platform”. The lead article is available at: https://medium.com/@markwkiehl/building-a-data-platform-on-gcp-0427500f62e8

Part One “Building a Data Platform on GCP” defined the functional requirements, and detailed how to install the required software.

Part Two “GCP Infrastructure & Authentication” explained how to use Google application default credentials (ADC) to authenticate a user-managed service account.

Part Three “Google Cloud Pub/Sub Messaging” showed how to use a Python script to generate and subscribe to the Google Pub/Sub Messaging service.

Part Four “Containerization using Docker” covered how to build a local Docker image for a Python script, run it locally, and then push it to Google Artifact Registry (repository).

Part Five “Google Cloud Run Jobs & Scheduler” demonstrated how to configure Google Cloud Run Jobs and Cloud Scheduler Jobs using Google CLI to execute a Python script stored in Google Artifact Registry on a specified interval from any Google region.

Part Six “Google BigQuery Cloud Database” set up a Google BigQuery dataset and table using the Google CLI, and then a Python script was used to write and query data with SQL.

Part Seven “Google Cloud Analytics” explored how to extract data from a Google BigQuery table, load it into a Pandas DataFrame, and effortlessly perform analysis and visualizations — all from a Python script.

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