Cloud Tools and Platforms Covered in a Multi-Cloud Course (AWS, Azure, GCP)
As more organizations embrace digital transformation, the demand for professionals skilled in multi-cloud environments has surged. A Multi-Cloud Engineer Course is designed to equip learners with hands-on experience and knowledge across the leading cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). But what exactly do you learn in terms of tools and services within each of these platforms? Let’s break it down.
1. Amazon Web Services (AWS)
AWS is the market leader in cloud computing and forms a major part of any multi-cloud curriculum. Here are key tools and services typically covered:
EC2 (Elastic Compute Cloud): Learn to provision and manage virtual servers.
S3 (Simple Storage Service): Cloud object storage for backups, data lakes, and applications.
VPC (Virtual Private Cloud): Networking setup and configurations.
RDS (Relational Database Service): Managed databases like MySQL, PostgreSQL, and more.
IAM (Identity and Access Management): User roles, policies, and security configurations.
CloudFormation & Terraform: Infrastructure as Code (IaC) for deploying AWS resources.
CloudWatch & CloudTrail: Monitoring, logging, and auditing AWS environments.
You'll also work with AWS CLI, Lambda (serverless computing), and Elastic Load Balancer (ELB) to build, scale, and monitor cloud-native applications.
2. Microsoft Azure
Azure is another top cloud provider known for its enterprise integrations, especially with Windows-based systems. Tools and services covered include:
Azure Virtual Machines (VMs): Setting up and managing virtual servers.
Azure Blob Storage: Scalable cloud storage for unstructured data.
Azure Virtual Network (VNet): Secure network configurations.
Azure SQL Database: A managed relational database service.
Azure Resource Manager (ARM) & Terraform: Automating deployments using templates and IaC.
Azure Active Directory (AD): Identity and access management across resources.
Azure Monitor & Log Analytics: For performance monitoring and diagnostics.
Learners also explore Azure DevOps for CI/CD pipeline integration, making it easier to manage deployments in a multi-cloud setup.
3. Google Cloud Platform (GCP)
GCP has gained popularity for its robust analytics, machine learning capabilities, and Kubernetes support. Key GCP services covered in a multi-cloud course include:
Compute Engine: For provisioning virtual machines.
Cloud Storage: Scalable object storage.
VPC Networks & Firewalls: Secure networking across zones.
Cloud SQL & BigQuery: Database services for transactional and analytical data.
IAM & Cloud Identity: Access control and security best practices.
Deployment Manager & Terraform: Managing resources through templates and automation.
Stackdriver (now Cloud Operations): Monitoring and logging for infrastructure health.
The course also covers Google Kubernetes Engine (GKE), as Kubernetes is a central component in multi-cloud application deployment.
Conclusion
A comprehensive Multi-Cloud Engineer Course prepares you to confidently work across AWS, Azure, and GCP—each with its own unique tools, architectures, and best practices. By learning to navigate and integrate services from all three providers, you become a highly versatile cloud professional ready for today’s hybrid IT environments. Whether you’re building scalable applications or designing resilient infrastructure, mastering these tools gives you a competitive edge in the cloud job market.
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