Google Cloud: What Is Google Cloud Platform?

What is Google Cloud Platform?

Google Cloud Platform (GCP) is Google's suite of cloud services, progressively launched from 2008 with App Engine, the first major PaaS service on the market. The third-largest global cloud computing provider with approximately 11% market share, GCP distinguishes itself through its unique technological heritage. Google Cloud services are built on the same infrastructure that powers products used by billions of people: Google Search, YouTube, Gmail, and Google Maps.

What fundamentally differentiates GCP from its competitors is its excellence in data and artificial intelligence. BigQuery, Google's serverless data warehouse, is recognised as one of the most powerful data analytics tools available. TensorFlow, Vertex AI, and TPUs (Tensor Processing Units) offer unmatched machine learning capabilities. Google's global network, with its private submarine cables, ensures exceptionally low latency between regions.

At Kern-IT, we recommend GCP when a project has a strong data, machine learning, or analytics component. For standard Django applications, GCP offers services comparable to AWS and Azure with Cloud Run (serverless containers), Cloud SQL (managed databases), and Cloud Storage. Our agnostic approach allows us to choose the provider best suited to each client's context.

Why Google Cloud Matters

GCP has earned its place in the cloud market by differentiating on strategic axes where Google possesses an indisputable technological advantage.

• Leader in data and analytics: BigQuery can analyse petabytes of data in seconds, without provisioning infrastructure. This serverless service revolutionises the analytics approach for companies processing large data volumes.
• AI and ML excellence: Vertex AI, AutoML, and prebuilt APIs (Vision, Natural Language, Translation) are among the most advanced on the market. Access to TPUs offers unbeatable model training performance.
• Native Kubernetes: Google created Kubernetes, and GKE (Google Kubernetes Engine) is widely considered the most mature and performant managed Kubernetes service. For teams deploying on Kubernetes, GKE offers a superior experience.
• Premium global network: Google's private network interconnects its data centres via proprietary submarine cables, offering latency and bandwidth superior to most competitors.
• Innovative pricing: automatic Sustained Use Discounts apply without commitment, and Committed Use Discounts offer reductions of up to 57% for predictable workloads.

How It Works

GCP organises its resources in an Organisation > Folders > Projects hierarchy. Each project is a logical container that groups resources, APIs, and billing settings. This structure enables fine-grained access and cost management by team or application.

Compute services cover multiple levels of abstraction. Compute Engine provides virtual machines (IaaS). Google Kubernetes Engine (GKE) orchestrates containers. Cloud Run executes containers in a serverless manner, without managing a cluster. App Engine hosts web applications in a fully managed way (PaaS). Cloud Functions executes code in response to events (FaaS).

Services are accessed via the web console, the Google Cloud SDK (gcloud CLI), REST APIs, or client libraries available in Python, Java, Go, and other languages. Terraform is fully supported for Infrastructure as Code, and Deployment Manager offers a native alternative. Security relies on IAM for identity and role management, with a least-privilege model by default.

Concrete Example

For a web analytics project developed by Kern-IT, we used GCP for its data processing power. The Django application collects navigation and user behaviour data via a REST API. This data is stored in Cloud SQL (PostgreSQL) for transactional operations and regularly exported to BigQuery for large-scale analysis.

Dashboards built with Looker Studio (formerly Data Studio) visualise trends in real time. A Vertex AI pipeline periodically trains churn prediction models, whose results are exposed via a Cloud Run API. The Django application itself is deployed on Cloud Run, enabling automatic scaling from zero to hundreds of instances based on traffic, with billing only for actual execution time.

Implementation

1. Create a GCP account: sign up at cloud.google.com. Google offers $300 in free credits for 90 days and a generous permanent free tier.
2. Structure projects: create a GCP organisation and define a hierarchy of folders and projects. Separate environments (dev, staging, prod) in distinct projects with dedicated budgets.
3. Install the SDK: install the Google Cloud SDK (gcloud) to interact with GCP from the command line. Configure authentication and the default project.
4. Deploy the application: for a Django application, Cloud Run is often the most pragmatic choice. Containerise the application with Docker, push the image to Artifact Registry, and deploy to Cloud Run in a few commands.
5. Provision services: add Cloud SQL for PostgreSQL, Cloud Storage for files, and Cloud CDN for static assets. Use Terraform to automate provisioning.
6. Set up monitoring: enable Cloud Monitoring and Cloud Logging for surveillance. Configure alerts on key metrics and use Cloud Trace for distributed tracing.

Associated Technologies and Tools

• Cloud Run: serverless container execution, ideal for Django applications with variable traffic.
• Cloud SQL: managed relational databases (PostgreSQL, MySQL).
• BigQuery: serverless data warehouse for large-scale analytics.
• GKE: managed Kubernetes service, the most mature on the market.
• Vertex AI: unified platform for machine learning and generative AI.
• Cloud Storage: durable, scalable object storage.

Conclusion

Google Cloud Platform stands out for its excellence in data analytics, machine learning, and network infrastructure. For projects with a strong data or artificial intelligence component, GCP offers unmatched tools like BigQuery and Vertex AI. Its Cloud Run service greatly simplifies the deployment of containerised Django applications with a serverless model that optimises costs. At Kern-IT, we recommend GCP when a project's specific needs demand it, particularly for advanced analytics and machine learning, while maintaining a pragmatic and cloud-agnostic approach.