Module 8: Google Cloud Platform
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Google Cloud Platform is the third major hyperscaler, recognized for its leadership in Kubernetes, data analytics, and machine learning. This module introduces GCP’s core services, its strengths in data and AI, and how it compares to AWS.
We begin with GCP Core Services — Compute Engine (EC2 equivalent), Google Kubernetes Engine (GKE, the service that originated Kubernetes), Cloud Run (serverless containers), Cloud Storage (S3 equivalent), VPC, and Cloud Load Balancing. You will learn the gcloud CLI and the Google Cloud Console.
Next, GCP Data & AI covers the platform’s flagship services: BigQuery (serverless data warehouse), Dataflow (stream/batch processing based on Apache Beam), Pub/Sub (messaging), and Vertex AI (unified ML platform). GCP was built on the same infrastructure that powers Google Search, YouTube, and Gemini — its data and AI services are its strongest differentiator.
The GCP vs AWS section maps services, highlights GCP’s Kubernetes-native approach, and discusses Anthos for multicloud and hybrid deployments.
The module closes with a capstone project where you build a real-time data pipeline using Pub/Sub, Dataflow, and BigQuery.
Prerequisites
Module 1 (Cloud Fundamentals) and Module 2 (AWS Core Services) recommended. Basic familiarity with SQL and Python is helpful for the data pipeline project.
By the end of this module you will be able to manage GCP resources via CLI and console, compare GCP and AWS service models, leverage BigQuery and Dataflow for data processing, and deploy a streaming data pipeline.