๐ŸŒ

Google Cloud (GCP)

Google Cloud Platform is Google's cloud computing platform, spanning compute, storage, machine learning, and data-analytics services. It's the #9 most-used cloud tool in the world, reported in roughly 24.6% of developer surveys, and it's especially strong in data and analytics (BigQuery) and container orchestration โ€” Google originally created and open-sourced Kubernetes, which now runs natively as GKE. Data-heavy teams and ML practitioners favor it for that combination of managed data warehousing and cutting-edge AI tooling.

Quick facts
Type: Cloud computing platform (IaaS/PaaS/serverless)
Made by: Google
License: Proprietary, managed-cloud, pay-as-you-go pricing
Platforms/Hosting: Fully managed by Google across global data center regions; accessed via console, CLI, or SDKs
Primary use case: Hosting applications and infrastructure, with particular strength in big-data analytics (BigQuery) and Kubernetes-based container workloads
Jump to: ExampleGetting startedBest for

Example

The gcloud CLI manages Google Cloud resources from the terminal โ€” here's listing compute instances and querying BigQuery.

# list running Compute Engine instances
gcloud compute instances list

# deploy a container to Cloud Run (fully managed serverless)
gcloud run deploy my-service --image gcr.io/my-project/my-app --region us-central1

# run a query against BigQuery from the CLI
bq query --use_legacy_sql=false 'SELECT COUNT(*) FROM `my_dataset.events`'

Getting started

Create a free-tier Google Cloud account, install the gcloud CLI, then authenticate and set a default project.

# install the Google Cloud CLI, then initialize it
gcloud init

# authenticate your user account
gcloud auth login

# confirm the active project
gcloud config get-value project
Best for: Data-heavy and ML-focused teams that want a managed data warehouse (BigQuery) alongside first-class Kubernetes support โ€” a natural fit for analytics pipelines and AI workloads more than for teams simply wanting basic VM hosting.