Terraform is an open-source tool by HashiCorp that allows you to manage cloud infrastructure using code. Instead of manually setting up servers, databases, or networks, you define your infrastructure in configuration files using HashiCorp Configuration Language (HCL). Terraform then generates a plan to achieve your desired state and applies it automatically, ensuring your environments are consistent and reproducible.
It works with multiple cloud providers like AWS, Google Cloud, and Azure, which makes it ideal for multi-cloud setups or hybrid environments. For example, you could provision an AWS virtual machine, a Google Cloud database, and networking rules in Azure—all from the same codebase. Terraform is commonly used to automate deployments, manage staging and production environments, and support complex systems such as web applications or data pipelines. By integrating with version control and CI/CD pipelines, teams can maintain infrastructure safely and collaboratively.
Installing Terraform is easy; you need to navigate to the Terraform website and download the version for your operating system.
In my case, I installed Terraform for Windows. You need to download your correct version for the architecture of your computer’s processor_
Finally, the Download button downloads a ZIP file containing terraform.exe, the Terraform executable.
After downloading:
terraform.exe to a folder, for example C:\\Terraform.GCP, or Google Cloud Platform, is Google’s suite of cloud computing services, which includes computing, networking, databases, storage, AI/ML, and management tools. Essentially, GCS is the storage component of GCP, similar to how Amazon S3 works within AWS. Google Cloud Storage (GCS) is a scalable, secure, and highly available object storage service. GCS allows you to store and retrieve any amount of data at any time, from anywhere in the world. Unlike a traditional hard drive or database, GCS is designed to hold unstructured data such as images, videos, backups, logs, or CSV files, and makes it easy to integrate with applications, analytics pipelines, and machine learning workflows.
GCS is used in many scenarios, from backing up critical business data to hosting static websites or serving large datasets for analytics and machine learning projects. Because it’s cloud-based, it is highly reliable, scalable, and integrates seamlessly with other GCP services such as BigQuery, Dataflow, and AI Platform.