GoogleCloud Tutorials

Google Cloud Platform (GCP) Tutorials Roadmap

Section 1: Foundational Concepts and Core Services

  • Introduction to Cloud Computing:
    • Understanding cloud service models (IaaS, PaaS, SaaS).
    • Understanding cloud deployment models (Public, Private, Hybrid).
    • Benefits of cloud computing (scalability, elasticity, cost savings, agility).
  • Introduction to Google Cloud Platform (GCP):
    • GCP's history and position in the cloud market.
    • Overview of GCP's global infrastructure (Regions, Zones, Edge Locations).
    • Key differentiators of GCP.
  • GCP Account Setup:
    • Creating a Google Cloud account.
    • Understanding the Free Tier and Free Trial.
    • Setting up billing and budgets.
  • GCP Console and Command-Line Interface (gcloud):
    • Navigating the GCP Console (web-based interface).
    • Installing and configuring the gcloud command-line tool.
    • Using gcloud for interacting with GCP services.
  • Identity and Access Management (IAM):
    • Understanding IAM roles, permissions, and policies.
    • Managing users, groups, and service accounts.
    • Implementing the principle of least privilege.
  • Projects:
    • Understanding GCP projects as organizational units.
    • Creating and managing projects.
    • Linking projects to billing accounts.
  • Resource Hierarchy:
    • Understanding the hierarchy: Organization > Folders > Projects > Resources.
    • How IAM policies and Organization Policies are inherited.

Section 2: Compute Services

  • Compute Engine (IaaS):
    • Understanding Virtual Machines (VMs) on GCP.
    • Creating and managing VM instances.
    • Machine types and instance families.
    • Persistent Disks (storage for VMs).
    • Networking for Compute Engine (VPC networks, firewall rules).
    • Managing VM lifecycle.
    • Instance Templates and Managed Instance Groups (MIGs).
    • Autoscaling MIGs.
    • Load Balancing for Compute Engine.
  • Google Kubernetes Engine (GKE) (Container Orchestration):
    • Introduction to containers (Docker).
    • Introduction to Kubernetes concepts (Pods, Deployments, Services, Nodes, Clusters).
    • Creating and managing GKE clusters.
    • Deploying applications to GKE.
    • Scaling deployments.
    • Exposing applications (Services, Ingress).
    • Understanding GKE networking.
    • GKE Autopilot vs. Standard clusters.
  • Cloud Run (Serverless Containers):
    • Understanding serverless containers.
    • Deploying containerized applications to Cloud Run.
    • Automatic scaling based on requests.
    • Connecting services.
  • Cloud Functions (Function-as-a-Service - FaaS):
    • Understanding serverless functions.
    • Writing and deploying Cloud Functions (various languages).
    • Triggering functions (HTTP, Cloud Storage, Pub/Sub, etc.).
    • Connecting to other GCP services.
  • App Engine (PaaS):
    • Understanding Platform-as-a-Service.
    • App Engine Standard vs. Flexible environments.
    • Deploying web applications.
    • Automatic scaling.
    • Integration with other GCP services.

Section 3: Storage Services

  • Cloud Storage (Object Storage):
    • Understanding object storage concepts.
    • Creating and managing buckets.
    • Uploading and downloading objects.
    • Storage classes (Standard, Nearline, Coldline, Archive).
    • Object versioning and lifecycle management.
    • Access control (IAM, ACLs).
    • Signed URLs.
  • Persistent Disks (Block Storage for VMs):
    • Understanding block storage.
    • Creating, attaching, and detaching Persistent Disks to Compute Engine VMs.
    • Disk types (Standard, SSD, Balanced, Extreme).
    • Snapshots.
  • Filestore (Managed Network File Storage - NFS):
    • Understanding shared file systems.
    • Creating and managing Filestore instances.
    • Connecting to Compute Engine VMs.

Section 4: Database Services

  • Cloud SQL (Managed Relational Database):
    • Understanding relational databases (MySQL, PostgreSQL, SQL Server).
    • Creating and managing Cloud SQL instances.
    • Database administration tasks (backups, replication, scaling).
    • Connecting applications to Cloud SQL.
  • Cloud Spanner (Globally Distributed Relational Database):
    • Understanding globally distributed databases.
    • Key features of Cloud Spanner (consistency, scalability, availability).
    • Creating and managing Spanner instances and databases.
  • Firestore (NoSQL Document Database):
    • Understanding NoSQL document databases.
    • Firestore data model (collections, documents, subcollections).
    • Reading and writing data.
    • Querying data.
    • Integration with mobile and web applications (Firebase).
  • Bigtable (NoSQL Wide-Column Database):
    • Understanding wide-column databases.
    • Use cases for Bigtable (large operational and analytical data workloads).
    • Creating and managing Bigtable instances and tables.
  • Memorystore (Managed In-Memory Cache):
    • Understanding in-memory caching (Redis, Memcached).
    • Creating and managing Memorystore instances.
    • Integrating with applications for caching.

Section 5: Networking Services

  • Virtual Private Cloud (VPC) Networks:
    • Understanding VPC networks, subnets, and IP addressing.
    • Creating and managing VPC networks.
    • Firewall rules.
    • Routes.
    • Shared VPC.
    • VPC Network Peering.
  • Cloud Load Balancing:
    • Understanding different types of load balancers (Global, Regional, HTTP(S), TCP, UDP).
    • Setting up load balancers for Compute Engine, GKE, and other services.
    • Health checks.
  • Cloud DNS:
    • Understanding Domain Name System (DNS).
    • Managing DNS zones and records on GCP.
  • Cloud CDN (Content Delivery Network):
    • Understanding CDNs and caching.
    • Enabling Cloud CDN for your applications.
  • Cloud Interconnect / VPN:
    • Connecting your on-premises network to GCP.
    • Understanding Dedicated Interconnect, Partner Interconnect, and Cloud VPN.
  • Network Intelligence Center (Optional):
    • Understanding network monitoring and diagnostics tools.

Section 6: Big Data and Analytics Services

  • BigQuery (Serverless Data Warehouse):
    • Understanding data warehouses.
    • Loading data into BigQuery.
    • Querying data using standard SQL.
    • Understanding BigQuery architecture (storage and compute separation).
    • Managing datasets and tables.
    • BigQuery ML (Machine Learning).
  • Cloud Dataflow (Managed Data Processing):
    • Understanding batch and stream processing.
    • Introduction to Apache Beam.
    • Building and running Dataflow pipelines.
  • Cloud Dataproc (Managed Apache Hadoop/Spark):
    • Understanding Hadoop and Spark.
    • Creating and managing Dataproc clusters.
    • Running Spark and Hadoop jobs.
  • Cloud Pub/Sub (Messaging Service):
    • Understanding asynchronous messaging.
    • Creating topics and subscriptions.
    • Publishing and subscribing to messages.
    • Use cases for Pub/Sub (event-driven architectures, data pipelines).
  • Data Studio / Looker Studio (Data Visualization):
    • Connecting to data sources (BigQuery, etc.).
    • Creating reports and dashboards.
  • Dataplex (Data Lakehouse Management) (Optional):
    • Understanding data lakes and data lakehouses.
    • Managing data across various sources.

Section 7: Machine Learning and AI Services (Overview)

  • AI Platform / Vertex AI:
    • Understanding the MLOps lifecycle.
    • Training, deploying, and managing ML models.
    • Using Vertex AI Workbench (managed notebooks).
  • Pre-trained APIs:
    • Cloud Vision AI (image analysis).
    • Cloud Natural Language AI (text analysis).
    • Cloud Translation AI (language translation).
    • Cloud Speech-to-Text / Text-to-Speech AI.
    • Dialogflow (conversational interfaces).

Section 8: Developer Tools and Operations

  • Cloud Source Repositories:
    • Using managed Git repositories.
  • Cloud Build (CI/CD):
    • Building, testing, and deploying applications.
    • Creating build pipelines.
  • Cloud Deploy (CD):
    • Automating continuous delivery to GKE and Cloud Run.
  • Cloud Monitoring (formerly Stackdriver Monitoring):
    • Collecting metrics from GCP services.
    • Creating dashboards and charts.
    • Setting up alerting policies.
  • Cloud Logging (formerly Stackdriver Logging):
    • Collecting and viewing logs from GCP services.
    • Creating log-based metrics.
    • Exporting logs.
  • Cloud Trace (Distributed Tracing):
    • Understanding distributed tracing.
    • Analyzing latency in applications.
  • Cloud Profiler (Performance Analysis):
    • Identifying performance bottlenecks in code.

Section 9: Security and Management

  • Security Overview:
    • GCP's shared responsibility model.
    • Key security services.
  • Cloud Identity (Managed Users and Groups):
    • Managing identities integrated with GCP.
  • Resource Manager:
    • Managing the resource hierarchy (Organizations, Folders, Projects).
  • Organization Policy Service:
    • Setting constraints on GCP resource configurations.
  • Security Command Center (Optional):
    • Understanding security and data risk analysis.
  • Secrets Manager:
    • Storing and managing secrets securely.

Section 10: Cost Management and Billing

  • Understanding GCP Pricing:
    • How different services are priced.
    • Using the GCP Pricing Calculator.
    • Understanding sustained usage discounts and committed use discounts.
  • Monitoring Costs:
    • Using the Billing reports.
    • Setting up budget alerts.
  • Cost Optimization Strategies:
    • Choosing the right compute options.
    • Optimizing storage usage.
    • Right-sizing resources.

Section 11: Next Steps and Specializations

  • Explore Specific Use Cases:
    • Building web applications.
    • Setting up data pipelines.
    • Deploying microservices.
    • Implementing disaster recovery.
  • Deep Dive into Key Services:
    • Focus on the services most relevant to your goals (e.g., GKE for container orchestration, BigQuery for data analysis).
  • Prepare for GCP Certifications:
    • Cloud Digital Leader (Foundational)
    • Associate Cloud Engineer (Associate)
    • Professional Cloud Architect (Professional)
    • Professional Data Engineer (Professional)
    • Professional Cloud Developer (Professional)
    • Etc.
  • Explore Advanced Topics:
    • Networking (Hybrid Connectivity, Network Performance).
    • Security (VPC Service Controls, KMS).
    • Automation (Deployment Manager, Terraform).
    • Serverless patterns.
    • ML/AI model development and deployment.