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+91-834 161 1955 Mon-Fri 9am - 6pm
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Google Cloud Platform Training Key Features

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Practical GCP Cloud Labs

Get hands-on experience deploying and managing resources, setting up networking, and working with Google Cloud services in a real cloud environment.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated GCP Technical Support

Receive personalized assistance for all your Google Cloud projects and complex cloud challenges from our experienced instructors during and after your course.

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Robust Certification & Placement Guidance

We help you prepare for Google Cloud certifications and interviews with mock sessions, resume optimization, and direct connections to cloud engineering roles.

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Real-World GCP Deployment Projects

Gain invaluable experience by developing end-to-end cloud solutions, from infrastructure deployment to serverless applications, data services, and machine learning models on GCP.

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Dynamic GCP Learning Community

Collaborate with a supportive community of peers and instructors, fostering enhanced cloud skills, knowledge sharing, and valuable networking opportunities for career growth.

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Google Cloud Platform Training Overview

Value Learning offers comprehensive Google Cloud Platform (GCP) training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to equip you with the practical skills needed to deploy, manage, and scale applications and data solutions on Google's powerful cloud infrastructure.

Google Cloud Platform is a suite of cloud computing services that runs on the same infrastructure Google uses internally for its end-user products, such as Google Search and YouTube. GCP provides highly scalable and secure solutions for computing, storage, networking, big data, machine learning, and more. Our expert-led training covers fundamental GCP concepts, including Compute Engine, Cloud Storage, BigQuery, Cloud Functions, and Kubernetes Engine, ensuring you are proficient in designing and implementing various cloud architectures for modern enterprises.

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Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

Google Cloud Platform Training Objectives

The Google Cloud Platform course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a robust understanding of GCP cloud services and their practical applications for various business needs.

Through this training, you will gain hands-on experience with deploying and managing virtual machines, configuring storage solutions, implementing secure networking, and utilizing various GCP compute, database, and big data services like BigQuery and Cloud Pub/Sub. You'll also grasp essential cloud concepts, security best practices, and cost management within the Google Cloud ecosystem.

The primary goal of the training is to empower learners to confidently leverage GCP to build, deploy, and manage scalable, secure, and highly available cloud solutions, preparing them for official Google Cloud certifications and in-demand cloud roles.

To equip learners with comprehensive, practical experience in designing and implementing cloud architectures on GCP, covering everything from fundamental infrastructure as a service (IaaS) deployments to advanced serverless and containerized solutions, ensuring readiness for real-world cloud projects.

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Course Curriculum - Google Cloud Platform (GCP) Specialist

Overview:
  • What is Cloud Computing? IaaS, PaaS, SaaS Models
  • Benefits and Challenges of Cloud Adoption
  • Introduction to Google Cloud Platform: History, Global Infrastructure (Regions, Zones)
  • Core Strengths of GCP: AI/ML, Data Analytics, Open Source Focus
  • Navigating the GCP Console and `gcloud` CLI
  • GCP Projects, Billing Accounts, and Resource Hierarchy
  • Understanding Google's Shared Responsibility Model

  • GCP IAM: Principles, Roles (Primitive, Predefined, Custom), Members
  • Service Accounts: Purpose, Creation, and Best Practices
  • Understanding Resource Hierarchy and IAM Policy Inheritance
  • Managing Access with IAM Conditions
  • Introduction to Cloud Audit Logs and Security Command Center
  • Data Encryption in Google Cloud (Encryption at Rest, In Transit, CSEK, CMEK)
  • Hands-on: Configuring IAM for Projects and Resources, Creating Service Accounts

  • Compute Engine: Instances, Machine Types, Images, Persistent Disks
  • Managed Instance Groups (MIGs): Autoscaling, Autohealing, Load Balancing Integration
  • Understanding Preemptible VMs and Spot VMs for Cost Optimization
  • Sole-Tenant Nodes for Dedicated Hardware
  • Introduction to Serverless Compute: Cloud Functions (FaaS), Cloud Run (Containers as a Service)
  • App Engine: Standard vs. Flexible Environments for Web Applications
  • Hands-on: Launching VMs, Configuring Autoscaling, Deploying a Simple App to Cloud Run/App Engine

  • Introduction to GKE: Managed Kubernetes Service Benefits
  • GKE Cluster Architecture: Control Plane, Node Pools
  • Deploying Applications to GKE: Pods, Deployments, Services
  • Autoscaling in GKE: Cluster Autoscaler, Horizontal Pod Autoscaler (HPA)
  • GKE Autopilot vs. Standard Mode
  • Managing Helm Charts on GKE
  • Hands-on: Creating a GKE cluster, Deploying a multi-tier application, Scaling workloads

  • Cloud Storage (Object Storage): Buckets, Objects, Storage Classes (Standard, Nearline, Coldline, Archive)
  • Lifecycle Management for Cost Optimization
  • Persistent Disk (Block Storage): Types, Snapshots, Resizing
  • Filestore (Managed NFS File Storage) for shared access
  • Introduction to Storage Transfer Service
  • Data Migration Strategies to GCP Storage
  • Hands-on: Creating Storage Buckets, Uploading/Downloading Objects, Configuring Disk Snapshots

  • Cloud SQL: Managed Relational Database (MySQL, PostgreSQL, SQL Server)
  • Cloud Spanner: Globally Distributed Relational Database for High Scalability
  • Firestore: NoSQL Document Database for Web/Mobile Apps (Datastore Mode vs. Native Mode)
  • Cloud Bigtable: Wide-Column NoSQL for Large Analytical and Operational Workloads
  • BigQuery: Serverless Data Warehouse for Analytics (Concepts, Quering, Cost)
  • Choosing the Right GCP Database for Your Use Case
  • Hands-on: Provisioning a Cloud SQL instance, Storing data in Firestore, Running basic BigQuery queries

  • Virtual Private Cloud (VPC) Networks: Subnets, IP Addresses
  • Firewall Rules and Network Security Best Practices
  • Cloud Load Balancing: Global, Regional, Internal Load Balancers (HTTP(S), TCP, SSL Proxy, Network)
  • Cloud DNS: Managed DNS Service
  • Connecting On-Premises to GCP: Cloud VPN, Cloud Interconnect
  • Cloud CDN for Content Delivery
  • Understanding Network Tiers (Standard vs. Premium)
  • Hands-on: Configuring VPCs, Firewall Rules, and a Global HTTP(S) Load Balancer

  • Deep Dive into BigQuery: Advanced Querying, Data Loading, Partitioning, Clustering, Views
  • Dataflow: Managed Service for Apache Beam (Batch and Streaming ETL)
  • Dataproc: Managed Spark and Hadoop Service
  • Pub/Sub: Real-time Messaging Service for Event-Driven Architectures
  • Composer: Managed Apache Airflow for Workflow Orchestration
  • Looker (Introduction): Business Intelligence and Data Analytics Platform
  • Building Data Pipelines on GCP

  • Introduction to Google Cloud AI/ML Offerings
  • **Vertex AI:** Unified ML Platform (Training, Prediction, MLOps)
  • Pre-trained APIs: Vision AI, Natural Language AI, Speech-to-Text, Text-to-Speech
  • AutoML: Training Custom Models with Minimal Code
  • Generative AI on Google Cloud: Gemini Models, Vertex AI Search and Conversation
  • AI Infrastructure: TPUs (Tensor Processing Units)
  • Ethical AI Considerations

  • Deep Dive into Cloud Functions: Triggers, Runtimes, Use Cases
  • Deep Dive into Cloud Run: Container-based Serverless, Scalability, Event-Driven
  • App Engine: Designing and Deploying Scalable Web Applications
  • Cloud Endpoints and API Gateway for API Management
  • Cloud Build: CI/CD Service for Building and Deploying Applications
  • Artifact Registry: Universal Package Manager for Images and Artifacts (replacing Container Registry)
  • Hands-on: Building and Deploying Serverless Functions and Containerized Apps

  • Cloud Monitoring: Collecting Metrics, Creating Dashboards, Alerting
  • Cloud Logging: Centralized Log Management, Log Explorer, Log-based Metrics
  • Cloud Trace: Distributed Tracing for Latency Analysis
  • Cloud Debugger: Debugging Live Production Applications
  • Cloud Error Reporting: Aggregating and Analyzing Application Errors
  • Service Monitoring with SLOs/SLIs (Introduction to SRE concepts)
  • Hands-on: Setting up Monitoring Dashboards, Creating Alerts, Analyzing Logs

  • Introduction to Infrastructure as Code and its Benefits
  • Terraform Fundamentals: HCL Syntax, Providers, Resources, Data Sources
  • Managing GCP Resources with Terraform: `init`, `plan`, `apply`, `destroy`
  • Terraform State Management: Remote State (Cloud Storage Backend)
  • Terraform Modules for Reusability and Organization
  • Integrating Terraform into CI/CD Pipelines for Automated Deployments
  • Hands-on: Deploying a VPC, Compute Engine instances, and Storage Buckets using Terraform

  • Understanding Cloud Migration Methodologies (6 Rs: Rehost, Replatform, Rearchitect, etc.)
  • GCP Migration Services: Migrate for Compute Engine, Migrate for Anthos
  • Database Migration Service
  • Hybrid Cloud Architecture with Anthos: Overview and Use Cases
  • Connecting On-Premises with Cloud Interconnect and Cloud VPN
  • Managing Hybrid Environments with Cloud Operations
  • Best Practices for a Smooth Cloud Migration

  • Understanding GCP Pricing Models: Pay-as-you-go, Sustained Use Discounts (SUDs), Committed Use Discounts (CUDs)
  • Billing Accounts and Budgets
  • Cost Visibility and Reporting (Billing Reports, Cost Management APIs)
  • Resource Tagging and Labels for Cost Allocation
  • Strategies for Cost Optimization: Rightsizing, Automation, Spot/Preemptible VMs, Cold Storage
  • FinOps Principles in a GCP Context
  • Hands-on: Setting up Budgets and Alerts, Analyzing Billing Data with BigQuery

  • Designing for High Availability and Disaster Recovery on GCP
  • Scalability Patterns: Load Balancing, Autoscaling, Serverless
  • Security Best Practices: Network Security, Data Protection, IAM Policies
  • Cost-Effective Architecture Design
  • Operational Excellence: Monitoring, Logging, Automation
  • Well-Architected Framework on Google Cloud
  • Case Studies: Reviewing Reference Architectures for Common Workloads

  • Designing an End-to-End Solution for a Given Business Scenario (e.g., E-commerce, Data Analytics Pipeline, Mobile Backend)
  • Selecting Appropriate GCP Services and Architectures
  • Implementing the Solution using `gcloud` CLI and Terraform
  • Deploying Applications on GKE or Serverless Platforms
  • Configuring Networking, Storage, and Databases
  • Setting up Monitoring, Logging, and Alerts
  • Ensuring Security and Cost Optimization
  • Documenting the Architecture and Implementation
  • Presentation and Code Review of the Project

  • Common GCP Job Roles: Cloud Architect, Cloud Engineer, Data Engineer, DevOps Engineer, Security Engineer, ML Engineer
  • Building a Strong GCP-focused Resume and Portfolio
  • Interview Preparation Tips and Common Questions for GCP Roles
  • **Job Market Insights for Hyderabad, Telangana, India (as of June 2025):**
    • Growing demand for GCP professionals, as more enterprises adopt multi-cloud strategies or shift to Google Cloud.
    • Average salary for a **Google Cloud Architect in Hyderabad** is approximately **₹22 - ₹28 lakhs per annum**.
    • Entry-level GCP professionals can expect **₹10 - ₹15 lakhs per year**, while senior-level professionals can command **₹30 lakhs+ per year**.
    • Strong demand for skills in GKE, BigQuery, Vertex AI, Terraform, and general cloud security.
  • **Most In-Demand Google Cloud Certifications (2025):**
    • **Associate Cloud Engineer:** Foundational, hands-on experience.
    • **Professional Cloud Architect:** Design, develop, and manage robust, scalable, secure solutions. (Highly valued)
    • **Professional Data Engineer:** Design, build, operationalize, and secure data processing systems.
    • **Professional Cloud DevOps Engineer:** Building and maintaining CI/CD pipelines, automation on GCP.
    • **Professional Cloud Security Engineer:** Designing and implementing secure infrastructure on GCP.
    • **Professional Machine Learning Engineer:** Designing and building ML models on GCP.
    • **Cloud Digital Leader:** Broad understanding of cloud concepts and GCP (entry-level, non-technical).
  • Continuous Learning: Staying updated with new GCP services and features (especially in AI/ML).
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