About the course

Description:

A "Live GCP Project Experience" refers to practical, hands-on experience in working with Google Cloud Platform (GCP) to build, deploy, and manage cloud-based applications and infrastructure in a real-world production environment. GCP provides a wide range of services for computing, storage, networking, machine learning, and more, which can be used to solve various business challenges. Below is a detailed description of what such a project experience might include:

1. Cloud Infrastructure Setup & Management:

• Compute Engine: Setting up and managing Google Compute Engine (GCE) virtual machines to run applications or services, including choosing the right instance types, configuring auto-scaling, and managing VM configurations with instance templates.
• Google Kubernetes Engine (GKE): Deploying and managing containerized applications using GKE. This includes setting up Kubernetes clusters, managing deployments, scaling pods, ensuring high availability, and optimizing resource usage.
• App Engine: Using Google App Engine (GAE) for a fully managed platform to deploy web applications without worrying about underlying infrastructure. This includes configuring App Engine environments, handling automatic scaling, and enabling continuous delivery.

2. Storage & Database Solutions:

• Google Cloud Storage (GCS): Configuring GCS for scalable object storage to store and retrieve any amount of data. This includes setting up lifecycle policies, versioning, access controls, and managing storage class options for cost optimization.
• Cloud SQL: Setting up Cloud SQL (managed relational databases such as MySQL, PostgreSQL, SQL Server) for web and enterprise applications. This includes automating backups, configuring replication, and managing high availability.
• Cloud Spanner: Implementing Cloud Spanner for highly scalable, globally distributed, and strongly consistent databases that require horizontal scaling and ACID compliance for mission-critical workloads.
• Cloud Bigtable: Using Cloud Bigtable for large-scale, low-latency NoSQL database solutions, typically for time-series data or IoT data.

3. Compute & Serverless Architecture:

• Cloud Functions: Developing Google Cloud Functions, a serverless platform to run event-driven code without managing infrastructure. This includes creating functions that respond to HTTP requests, Cloud Pub/Sub events, or Cloud Storage events.
• Cloud Run: Deploying and managing Cloud Run, a fully managed compute platform that automatically scales containers. This is used for stateless applications and microservices that require automatic scaling based on incoming traffic.
• Cloud Dataflow: Implementing Cloud Dataflow for stream and batch data processing, integrating with tools like Apache Beam to process large datasets in real-time or in batch mode.

4. Automation & Infrastructure as Code (IaC):

• Google Cloud Deployment Manager: Writing Deployment Manager templates to automate the creation and management of GCP resources such as Compute Engine instances, Cloud Storage buckets, and Cloud SQL databases.
• Terraform (GCP Provider): Using Terraform to provision GCP infrastructure with IaC. This includes writing configuration files to automate the creation of virtual machines, Kubernetes clusters, networks, and storage resources.
• Cloud Build: Automating the CI/CD pipeline with Google Cloud Build to build, test, and deploy code across different GCP services.

5. Security & Identity Management:

• Identity and Access Management (IAM): Configuring IAM roles and policies to control access to GCP resources, ensuring that users and services have the correct permissions according to the principle of least privilege.
• Cloud Key Management (KMS): Using Cloud KMS to securely manage encryption keys for encrypting data at rest and ensuring compliance with regulatory standards.
• Cloud Security Command Center: Implementing Cloud Security Command Center to monitor and secure GCP environments, identify vulnerabilities, and respond to security incidents.

6. Networking & Content Delivery:

• Virtual Private Cloud (VPC): Configuring VPC to create isolated network environments, connecting different services such as Compute Engine VMs, Cloud Functions, and Cloud SQL. This includes managing subnets, firewall rules, and private IPs.
• Cloud Load Balancing: Setting up Global HTTP(S) Load Balancer and SSL Proxy Load Balancer to distribute traffic across GCP resources, enabling high availability and scalability for web applications and microservices.
• Cloud CDN: Using Cloud CDN to cache and deliver web content with low latency from global locations, improving user experience and reducing load times for content-heavy applications.
• Cloud Interconnect & VPN: Setting up Dedicated Interconnect or Cloud VPN to create private, secure connections between on-premises infrastructure and GCP resources.

7. Monitoring, Logging & Performance Optimization:

• Google Cloud Monitoring & Stackdriver: Implementing Cloud Monitoring (formerly Stackdriver) to monitor resource utilization, application performance, and network traffic. This includes setting up custom dashboards, creating alerts for threshold breaches, and optimizing system performance.
• Cloud Logging: Configuring Cloud Logging to collect logs from GCP services and applications, making it easier to troubleshoot issues and gain insights into system behavior.
• Cloud Trace & Cloud Profiler: Using Cloud Trace for distributed tracing of application performance and Cloud Profiler to continuously monitor and profile applications for performance bottlenecks.

8. Deployment & Continuous Integration/Continuous Deployment (CI/CD):

• Cloud Build & Cloud Deploy: Setting up and managing Cloud Build for building applications, running tests, and creating Docker images. Using Cloud Deploy for deploying applications to GKE, Cloud Run, or Compute Engine instances.
• Google Kubernetes Engine (GKE) CI/CD: Integrating GKE with CI/CD pipelines using tools like Cloud Build and GitLab CI to automatically deploy containerized applications to Kubernetes clusters, enabling seamless updates and rollbacks.

9. Cost Management & Optimization:

• Google Cloud Cost Management: Using Google Cloud Cost Management tools to monitor and optimize resource usage and spending. This includes setting budgets, monitoring usage patterns, and implementing cost-saving strategies like preemptible VMs or committed use contracts.
• Committed Use Discounts & Sustained Use Discounts: Implementing committed use and sustained use discounts to optimize long-term costs for virtual machine instances, storage, and database services.

10. Machine Learning & AI Services:

• AI Platform: Implementing Google Cloud AI Platform for building, training, and deploying machine learning models at scale. This includes integrating pre-built models like Cloud Vision, Natural Language API, or Speech-to-Text into applications.
• BigQuery & Data Analytics: Using BigQuery for fast, scalable analytics and data warehousing to process large datasets. This includes setting up ETL pipelines using Cloud Dataflow or integrating with third-party tools for real-time data processing.

Example of a Live GCP Project:

"In a recent project, I led the migration of an on-premises web application to GCP. We utilized Google Kubernetes Engine (GKE) to manage containerized microservices, ensuring that the application scaled dynamically based on traffic. For the backend, we moved the relational database to Cloud SQL for seamless management and high availability. Cloud Load Balancing was configured to distribute traffic across regions, and we used Cloud CDN for content delivery to improve performance globally. The CI/CD pipeline was automated with Cloud Build and Cloud Deploy, enabling continuous integration and deployment of application updates. Cloud Monitoring and Cloud Logging were set up to ensure proactive issue detection and troubleshooting, while IAM roles were configured to secure resource access. The migration improved application scalability, security, and cost efficiency."

Key Skills & Tools:

• Compute: Google Compute Engine, GKE, App Engine, Cloud Run, Cloud Functions.
• Storage: Google Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable.
• Networking: VPC, Cloud Load Balancing, Cloud CDN, Cloud Interconnect, VPN.
• Security: IAM, Cloud KMS, Security Command Center.
• Automation & IaC: Google Cloud Deployment Manager, Terraform, Cloud Build.
• CI/CD: Cloud Build, Cloud Deploy, GKE.
• Monitoring & Logging: Cloud Monitoring, Cloud Logging, Cloud Trace.
• Cost Management: Cloud Cost Management, Sustained and Committed Use Discounts.
• Machine Learning: AI Platform, BigQuery.

This type of GCP project experience highlights your ability to architect, deploy, and manage cloud-based solutions in GCP, ensuring high performance, scalability, security, and cost optimization in a production environment.