Host a static website on Cloud Storage
Create a bucket, enable static website hosting, upload index.html and 404.html, and set public read access safely for web objects only.
Cloud / Knowledge library
Google Cloud Platform (GCP)
a cloud platform for compute, data, and ai services
Why learn Google Cloud Platform (GCP)?
GCP helps teams build, deploy, and scale applications without managing most underlying infrastructure, while offering managed services for compute, storage, networking, analytics, ML/AI, and security.
Where it came from
2008: Google App Engine launched and introduced Google’s public cloud application platform. 2011–2012: Cloud Storage and Compute Engine expanded Google Cloud into broader IaaS. 2014: Kubernetes was open-sourced by Google; GKE became a major managed Kubernetes offering. 2016 onward: BigQuery adoption accelerated as serverless analytics became mainstream. 2020s: Hybrid and multi-cloud operations grew through products like Anthos and stronger platform tooling.
- 2008: Google App Engine launched and introduced Google’s public cloud application platform.
- 2011–2012: Cloud Storage and Compute Engine expanded Google Cloud into broader IaaS.
- 2014: Kubernetes was open-sourced by Google; GKE became a major managed Kubernetes offering.
- 2016 onward: BigQuery adoption accelerated as serverless analytics became mainstream.
- 2020s: Hybrid and multi-cloud operations grew through products like Anthos and stronger platform tooling.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Create a bucket, enable static website hosting, upload index.html and 404.html, and set public read access safely for web objects only.
- Containerize a small API, push image to Artifact Registry, deploy to Cloud Run in one region, and configure environment variables and unauthenticated access for testing.
- Publish JSON events to Pub/Sub, land transformed rows into BigQuery (via Dataflow or custom consumer), and write SQL to report daily event counts.
- Design service accounts for app runtime and CI/CD separately, grant only required roles, and verify denied actions for non-required permissions.
- Compare two deployment designs (always-on VM vs Cloud Run autoscaling), estimate main cost drivers, and justify the lower-risk option.
More ways to practice
Deploy a container API to Cloud Run
Containerize a small API, push image to Artifact Registry, deploy to Cloud Run in one region, and configure environment variables and unauthenticated access for testing.
Ingest and query event data
Publish JSON events to Pub/Sub, land transformed rows into BigQuery (via Dataflow or custom consumer), and write SQL to report daily event counts.
Apply least-privilege IAM
Design service accounts for app runtime and CI/CD separately, grant only required roles, and verify denied actions for non-required permissions.
Cost-aware architecture review
Compare two deployment designs (always-on VM vs Cloud Run autoscaling), estimate main cost drivers, and justify the lower-risk option.
Documentation and sources
- Google Cloud documentation (cloud.google.com)
- Google Cloud SDK (gcloud) docs (cloud.google.com)
- Google Cloud Architecture Center (cloud.google.com)
- Google Cloud training and certifications (cloud.google.com)
Catalog research notes
- Continued expansion of generative AI tooling in Vertex AI, including stronger evaluation and safety workflows.
- Cloud Run has broadened serverless use cases with jobs, better scaling controls, and GPU availability in supported regions.
- BigQuery keeps improving open-data interoperability and lakehouse-style analytics patterns.
- Security and governance features continue consolidating around policy automation and centralized posture visibility.
Connect the ideas
Explore next
Real World Example
Automated Inventory Forecasting for Retail Logistics
A mid-sized regional retail chain managing thousands of SKUs across multiple warehouse locations.
The business problem
The company struggles with manual inventory replenishment cycles, leading to frequent stockouts of high-demand items and overstocking of slow-moving goods, resulting in high operational overhead.
How this topic solves it
By leveraging Google Cloud Platform's data analytics and AI services, the retailer can ingest historical sales data into a managed data warehouse, process it through machine learning pipelines, and generate automated replenishment triggers.
- Ingest historical sales and inventory logs into a centralized Google Cloud data warehouse.
- Develop a machine learning model using AI services to identify seasonal demand patterns.
- Configure automated data pipelines to transform raw logs into actionable inventory forecasts.
- Deploy a serverless compute function to trigger purchase orders when stock levels fall below predicted thresholds.
The flow of data from raw retail logs to automated procurement actions.
Raw historical transaction data
Centralized storage for analytics
Predictive engine for demand forecasting
Serverless logic for order execution
- Sales Logs → Ingestion → Data Warehouse
- Data Warehouse → Training Data → AI Model
- AI Model → Threshold Alert → Compute Function
Expected business outcome
Improved inventory turnover rates and reduced manual intervention in the supply chain procurement process.
Limitations and checks
The primary limitation is the dependency on high-quality historical data; if input logs are incomplete or contain anomalies, the model will produce inaccurate forecasts. Users can check for this by monitoring data ingestion error rates and validating model drift against actual sales performance.