GCP Services
Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google that enables businesses to run their applications, store data, and leverage advanced technologies like machine learning, artificial intelligence, and big data analytics. GCP is built to offer flexible, scalable, and reliable infrastructure, which makes it a popular choice for enterprises looking to innovate and manage their workloads efficiently.
Choosing the right GCP services requires aligning your specific business needs with the capabilities of available services.
Compute Services
Compute Engine: Provides highly customizable and scalable virtual machines that run in Google’s data centers. Supports a variety of configurations, operating systems, and workloads.
Google Kubernetes Engine (GKE): A managed Kubernetes service that provides orchestration of containerized applications. Enables auto-scaling and simplified management of clusters.
Cloud Run Containers: Fully managed serverless platform designed to run containerized applications directly with automatic scaling and pay-per-use pricing.
Cloud Functions: Fully managed serverless, allows to run event-driven code without managing servers. It automatically scales based on demand and integrates with various Google Cloud services, making it ideal for microservices, automation, and lightweight backend processing.
App Engine: A platform-as-a-service (PaaS) for building and deploying scalable web applications, supporting multiple programming languages and automatic scaling.
Compute Decision Tree
Compute Decision Tree
├── 1. Define the Workload Type
│ ├── Static Workload (Fixed resources, no scaling needed)
│ │ └── Compute Option: Virtual Machines (VMs)
│ │ └── Use Case: Predictable, steady workloads with minimal fluctuation
│ └── Dynamic Workload (Fluctuating resources, variable load)
│ └── Compute Option: Autoscaling Solutions
│ └── Use Case: Applications with varying resource requirements
│
├── 2. Application Requirements
│ ├── High-Performance Computing (HPC) or GPU Intensive
│ │ └── Compute Option: Specialized VM (e.g., GPU VMs)
│ │ └── Use Case: AI/ML, 3D rendering, and scientific computing
│ └── General Purpose Compute (Web servers, basic apps)
│ └── Compute Option: Standard VM
│ └── Use Case: Websites, databases, basic applications with moderate resource needs
│
├── 3. Deployment Model
│ ├── Serverless (No server management, Access)
│ │ ├── Execution limits Option: Cloud Run functions
│ │ │ └── Use Case: Short-lived tasks, event-driven systems
│ │ └── Container-based Option: Cloud Run
│ │ └── Use Case: Docker Containerized applications
│ ├── K8s Containers
│ │ └── K8s Option: Google Kubernetes Engine
│ │ └── Use Case: K8s based applications with high scalability
│ ├── Virtual Machines (IaaS)
│ │ └── Compute Option: Google Compute Engine
│ │ └── Use Case: Full control over infrastructure, legacy applications
│ └── Fully Managed Platform (PaaS)
│ └── Compute Option: Google App Engine
│ └── Use Case: Fast deployment of web applications with minimal configuration
│
├── 4. Performance Requirements
│ ├── High Availability, Low Latency
│ │ └── Compute Option: Regional Managed Services (e.g., Google Cloud Spanner, Google Kubernetes Engine)
│ │ └── Use Case: Services that require low latency and high availability
│ └── Variable/On-Demand Performance
│ └── Compute Option: Preemptible VMs or Serverless
│ └── Use Case: Cost-saving solutions for burst workloads or non-critical services
│
└── 5. Cost Considerations
├── Low-cost, Short-term, or Irregular Usage
│ └── Compute Option: Preemptible VMs or Serverless
│ └── Use Case: Temporary, bursty workloads where cost is a priority
└── Predictable Budget with High Resource Usage
└── Compute Option: Committed Use Contracts or Autoscaling VMs
└── Use Case: Long-term projects where cost optimization is needed
Storage Services
Cloud Storage: Object storage service offering unlimited storage for unstructured data, with different storage classes for cost optimization and high durability and archiving.
Persistent Disks: High-performance block storage for virtual machines, with SSD and HDD options to suit different workloads.
Filestore: Fully managed file storage service for applications that require a shared file system, supporting NFS protocol.
Cloud Storage Transfer Service: Facilitates data transfers between cloud storage and on-premises systems or between different cloud environments.
Storage Decision Tree
GCP Storage Decision Tree
├── 1. Define the Storage Use Case
│ ├── If the storage is for structured data (e.g., VM disks, databases)
│ │ → Use Persistent Disks (SSD or HDD)
│ │ → Persistent Disks: High-performance block storage for VM disks and databases
│ ├── If the storage is for unstructured data (e.g., files, media, backups)
│ │ → Use Cloud Storage (Standard, Nearline, Coldline, Archive)
│ │ → Cloud Storage: Suitable for storing large files, backups, and media content
│ └── If the storage is for analytical or big data workloads
│ → Use Cloud Storage + BigQuery
│ → Cloud Storage + BigQuery: For storing and analyzing large datasets
│
├── 2. Performance and Latency Requirements
│ ├── If the storage needs high throughput and low latency
│ │ → Use Persistent SSDs (e.g., Local SSD or Persistent SSD Disks)
│ │ → Persistent SSDs: Fast read/write speeds for high-performance workloads
│ ├── If the storage needs moderate throughput and standard latency
│ │ → Use Persistent HDDs (Standard Persistent Disks)
│ │ → Persistent HDDs: Suitable for less demanding workloads with moderate throughput
│ └── If the storage needs to be event-driven with on-demand access
│ → Use Cloud Functions with Cloud Storage
│ → Cloud Functions + Cloud Storage: For serverless, event-driven storage access
│
├── 3. Data Access Patterns
│ ├── If the storage needs frequent access with fast reads and writes
│ │ → Use Persistent Disks (SSD)
│ │ → Persistent Disks: Fast, reliable storage for databases and VM applications
│ ├── If the storage needs infrequent access with high volume
│ │ → Use Cloud Storage (Coldline, Archive)
│ │ → Cloud Storage: Suitable for archival data, backups, and infrequent access
│ └── If the storage needs to support batch data access and analytics
│ → Use Cloud Storage + BigQuery
│ → Cloud Storage + BigQuery: For storing and analyzing large datasets
│
├── 4. Durability and Availability Requirements
│ ├── If the storage needs extremely high durability and global availability
│ │ → Use Cloud Storage (Multi-Regional)
│ │ → Cloud Storage (Multi-Regional): For global applications requiring high availability
│ └── If the storage needs moderate durability and local availability
│ → Use Cloud Storage (Regional)
│ → Cloud Storage (Regional): Suitable for regional applications with less critical needs
│
├── 5. Cost Considerations
│ ├── If the storage needs to be cost-effective for large-scale storage
│ │ → Use Cloud Storage (Coldline, Archive)
│ │ → Cloud Storage (Coldline, Archive): For cost-effective archival storage
│ ├── If the storage needs to be flexible and cost-efficient for regular access
│ │ → Use Persistent SSDs or Cloud SQL
│ │ → Persistent SSDs: For regular storage needs with high throughput
│ │ → Cloud SQL: For relational data with moderate access requirements
│ └── If the storage needs temporary, short-term use
│ → Use Preemptible VMs with Cloud Storage (Nearline)
│ → Preemptible VMs + Cloud Storage: For cost-effective, short-term storage
│
└── 6. Security and Compliance Requirements
├── If the storage needs to comply with sensitive or regulated data (e.g., healthcare, finance)
│ → Use encrypted Cloud Storage with compliance features
│ → Cloud Storage with encryption: For sensitive data with strict compliance requirements
└── If the storage needs baseline security
→ Use standard Cloud Storage or Persistent Disks
→ Standard Cloud Storage or Persistent Disks: For general-purpose applications with standard security
Networking Services
Virtual Private Cloud (VPC): Provides a custom networking environment for your GCP resources with fine-grained control over IP ranges and subnets.
Cloud Load Balancing: Distributes traffic across multiple backend resources globally to ensure high availability and performance.
Cloud CDN: Delivers web and video content to users with reduced latency by leveraging Google’s globally distributed edge network.
Cloud VPN: Securely connects your on-premises network to GCP resources via IPsec encryption.
Networking Decision Tree
GCP Networking Decision Tree
├── 1. Define the Networking Use Case
│ ├── If you need to connect multiple GCP services and manage network traffic between them
│ │ → Use Virtual Private Cloud (VPC)
│ │ → VPC: A private network that allows you to control IP address ranges, subnets, and routing between GCP resources
│ ├── If you need to connect your on-premises network to GCP securely
│ │ → Use Cloud Interconnect or Cloud VPN
│ │ → Cloud Interconnect: High-throughput, low-latency connection between on-premises and GCP
│ │ → Cloud VPN: Secure, encrypted connection over the public internet for hybrid cloud architectures
│ ├── If you need to distribute traffic across multiple instances, regions, or backends
│ │ → Use Global Load Balancing
│ │ → Global Load Balancing: Distributes incoming traffic to different instances or regions, supporting auto-scaling
│ └── If you need private communication between GCP resources without exposing them to the internet
│ → Use Private Google Access
│ → Private Google Access: Allows instances in a VPC to access Google APIs and services without going through the internet
│
├── 2. Traffic Distribution and Load Balancing
│ ├── If the traffic needs to be distributed globally with auto-scaling
│ │ → Use Global HTTP(S) Load Balancing
│ │ → Global HTTP(S) Load Balancing: Distributes HTTP/S traffic across global instances with automatic scaling
│ ├── If the traffic needs to be distributed regionally for non-HTTP/S protocols (e.g., TCP, UDP)
│ │ → Use Regional Load Balancing
│ │ → Regional Load Balancing: Load balancing for TCP/UDP traffic within a specific region
│ └── If the traffic needs to be balanced based on user location for low-latency access
│ → Use Cloud CDN + Load Balancing
│ → Cloud CDN + Load Balancing: Use Cloud CDN for caching content closer to users with load balancing for low latency
│
├── 3. Security and Access Control
│ ├── If you need to control network-level security between VMs or services
│ │ → Use VPC Firewall Rules
│ │ → VPC Firewall Rules: Control ingress and egress traffic to and from instances within a VPC
│ ├── If you need to ensure secure, encrypted communication between services
│ │ → Use Cloud Load Balancer + SSL/TLS termination
│ │ → Cloud Load Balancer with SSL/TLS: Secure traffic between clients and backends with SSL/TLS termination
│ └── If you need to secure access between GCP resources and external services
│ → Use Identity-Aware Proxy (IAP)
│ → Identity-Aware Proxy: Secures applications and resources by allowing access based on user identity
│
├── 4. Hybrid and Multi-cloud Connectivity
│ ├── If you need direct, high-speed connectivity between your on-premises network and GCP
│ │ → Use Dedicated Interconnect
│ │ → Dedicated Interconnect: Provides high-bandwidth, low-latency connections between on-premises and GCP
│ ├── If you need a secure, encrypted connection over the internet between on-premises and GCP
│ │ → Use Cloud VPN
│ │ → Cloud VPN: Creates a secure connection over the public internet to link on-premises to GCP
│ └── If you need a secure connection between multiple cloud providers and GCP
│ → Use Cloud Interconnect Partner
│ → Cloud Interconnect Partner: Enables connections to GCP through a third-party service provider
│
├── 5. Network Monitoring and Management
│ ├── If you need to monitor network traffic and analyze performance
│ │ → Use Network Intelligence Center
│ │ → Network Intelligence Center: Provides insights into network performance, traffic flow, and issues
│ ├── If you need to manage and track network configurations and policies
│ │ → Use GCP Cloud Operations (formerly Stackdriver)
│ │ → Cloud Operations: For logging, monitoring, and alerting on network resources and services
│ └── If you need to visualize network topologies and flows
│ → Use GCP Cloud Console Network Dashboard
│ → Cloud Console Network Dashboard: Provides a visual representation of your network's topology and traffic patterns
│
└── 6. Network Automation and Configuration
├── If you need automated network provisioning and management
│ → Use Infrastructure as Code (Terraform + GCP)
│ → Terraform + GCP: Use Terraform for automating infrastructure deployment, including networking services
├── If you need to automate load balancing and scaling
│ → Use GCP Load Balancer + Autoscaler
│ → Load Balancer + Autoscaler: Automates scaling and load balancing based on real-time traffic
└── If you need a fully managed, scalable network service without infrastructure management
→ Use Cloud Run with Serverless VPC Access
→ Cloud Run + Serverless VPC Access: For deploying and running containers without managing the network infrastructure
Database Services
Cloud SQL: Fully managed relational database service supporting MySQL, PostgreSQL, and SQL Server, with features like automated backups and high availability.
Cloud Spanner: Horizontally scalable and globally consistent relational database service, ideal for mission-critical applications.
Firestore: A NoSQL document database built for real-time, offline-first mobile and web apps.
Bigtable: NoSQL key-value and wide-column database designed for high throughput and large-scale operational and analytical workloads.
BigQuery: Data Warehouse (Columnar Storage) primarily used for analytics and business intelligence (BI) applications that require querying large volumes of structured and semi-structured data, such as data lakes, data warehousing, and analytical dashboards.
Cloud Memorystore: In-Memory Data Store used for caching and improving the performance of applications, such as session management, gaming leaderboards, and recommendation engines.
Database Decision Tree
GCP Database Decision Tree
├── 1. Define the Database Use Case
│ ├── Relational Data (e.g., complex queries, transactions)
│ │ └── Database Option: Cloud SQL, Cloud Spanner
│ │ └── Use Case: Transactional systems, relational data with ACID properties
│ ├── NoSQL Data (e.g., schema-less, document or key-value stores)
│ │ └── Database Option: Firestore, Bigtable
│ │ └── Use Case: Flexible data models, high scalability with low latency
│ └── Analytical/Big Data (e.g., large-scale data processing)
│ └── Database Option: BigQuery
│ └── Use Case: Analytics, data warehousing, machine learning
│
├── 2. Performance and Scalability Requirements
│ ├── High Availability, Global Distribution
│ │ └── Database Option: Cloud Spanner
│ │ └── Use Case: Global, horizontally scalable databases with high consistency
│ ├── Moderate Availability, Regional Deployment
│ │ └── Database Option: Cloud SQL
│ │ └── Use Case: Relational database needs with lower complexity
│ └── High Throughput, Low Latency for NoSQL
│ └── Database Option: Bigtable
│ └── Use Case: Low-latency access to large-scale, high-volume NoSQL data
│
├── 3. Data Access Patterns
│ ├── Frequent, Complex Queries
│ │ └── Database Option: Cloud SQL, Cloud Spanner
│ │ └── Use Case: OLTP workloads, relational queries with joins and transactions
│ ├── Real-Time, High Volume Reads/Writes
│ │ └── Database Option: Firestore, Bigtable
│ │ └── Use Case: Real-time applications with low-latency access
│ └── Batch Data, Data Warehousing
│ └── Database Option: BigQuery
│ └── Use Case: ETL jobs, large-scale data analysis, reports
│
└── 4. Cost Considerations
├── Cost-Effective, Small-Scale
│ └── Database Option: Cloud SQL (MySQL, PostgreSQL)
│ └── Use Case: Small to medium-sized apps with moderate database requirements
├── Cost-Effective, Highly Scalable
│ └── Database Option: Firestore
│ └── Use Case: Serverless NoSQL solutions for variable workloads
└── Enterprise-Level, High Availability
└── Database Option: Cloud Spanner
└── Use Case: High availability, scalability for global apps
AI and Machine Learning
Vertex AI: End-to-end platform for building, deploying, and scaling machine learning models using integrated tools and pre-trained APIs.
Cloud AI APIs: Pre-trained APIs for natural language processing, computer vision, translation, and speech-to-text capabilities.
AI Platform: Provides tools and services for training, tuning, and deploying machine learning models in the cloud.
AI/ML Decision Tree
GCP AI/ML Decision Tree
├── 1. Define the AI/ML Use Case
│ ├── If you need a fully managed service to build custom machine learning models without handling infrastructure
│ │ → Use AI Platform (Vertex AI)
│ │ → Vertex AI: Managed environment for training, deploying, and managing custom ML models
│ ├── If you need to build custom models using a framework like TensorFlow, PyTorch, or XGBoost
│ │ → Use AI Platform Notebooks or AI Platform Training
│ │ → AI Platform Notebooks: Managed Jupyter notebooks for ML development and collaboration
│ │ → AI Platform Training: Managed training for custom models with support for popular ML frameworks
│ ├── If you need pre-trained models for specific tasks (e.g., image recognition, translation)
│ │ → Use Google Cloud AutoML
│ │ → Google Cloud AutoML: Pre-trained models that you can customize with minimal ML expertise
│ └── If you need to process large-scale unstructured data (e.g., images, text) and perform specific ML tasks
│ → Use TensorFlow or Keras on AI Platform
│ → TensorFlow or Keras: Frameworks for developing scalable deep learning models
│
├── 2. Model Deployment and Inference
│ ├── If you need a fully managed service for model deployment and real-time predictions at scale
│ │ → Use Vertex AI Predictions
│ │ → Vertex AI Predictions: Managed service for deploying models and serving predictions at scale
│ ├── If you need to deploy models for batch predictions or long-running jobs
│ │ → Use AI Platform Batch Prediction
│ │ → AI Platform Batch Prediction: For running predictions on large datasets in batch mode
│ └── If you need an edge solution for running ML models locally on devices (e.g., IoT devices)
│ → Use Edge TPU + TensorFlow Lite
│ → Edge TPU + TensorFlow Lite: Accelerates ML inference at the edge on devices like Raspberry Pi
│
├── 3. Pre-built Models for Specific Tasks
│ ├── If you need natural language processing (e.g., sentiment analysis, text classification)
│ │ → Use Natural Language API
│ │ → Natural Language API: Pre-built models for analyzing and extracting insights from text
│ ├── If you need speech-to-text or text-to-speech capabilities
│ │ → Use Speech-to-Text API or Text-to-Speech API
│ │ → Speech-to-Text API: Converts audio speech into text
│ │ → Text-to-Speech API: Converts text into natural-sounding speech
│ ├── If you need image analysis (e.g., object detection, image classification)
│ │ → Use Vision AI
│ │ → Vision AI: Pre-trained models for image recognition and analysis
│ └── If you need translation or multilingual text support
│ → Use Translation API
│ → Translation API: Automatically translates text between multiple languages
│
├── 4. Data and Model Training
│ ├── If you need to process large-scale structured data with ML models
│ │ → Use BigQuery ML
│ │ → BigQuery ML: Run machine learning models directly on large datasets in BigQuery without moving data
│ ├── If you need to train custom models with large datasets in a fully managed environment
│ │ → Use Vertex AI Workbench
│ │ → Vertex AI Workbench: A managed environment for building, training, and deploying ML models
│ └── If you need data labeling and preparation for training models
│ → Use Data Labeling Service
│ → Data Labeling Service: Enables human labeling of large datasets for supervised learning tasks
│
├── 5. Model Management and Monitoring
│ ├── If you need to manage and monitor deployed models and track model performance
│ │ → Use Vertex AI Model Monitoring
│ │ → Vertex AI Model Monitoring: Monitors and logs model performance over time in production
│ ├── If you need a tool to evaluate, compare, and deploy ML models efficiently
│ │ → Use Vertex AI Model Registry
│ │ → Vertex AI Model Registry: Centralized registry to store and manage model versions
│ └── If you need to improve and update models after deployment (e.g., retraining)
│ → Use Vertex AI Continuous Monitoring & Retraining
│ → Vertex AI Continuous Monitoring: Automatically retrains models based on new data
│
└── 6. Cost Considerations
├── If you need cost-effective solutions for small ML tasks and limited use cases
│ → Use Cloud AutoML
│ → Cloud AutoML: For small-scale tasks, customizable pre-trained models with minimal cost
├── If you need flexibility to scale and handle enterprise-level ML workloads
│ → Use Vertex AI
│ → Vertex AI: For scalable, cost-optimized AI/ML pipelines and model management
└── If you need a pay-as-you-go model for training and serving ML models
→ Use AI Platform (Vertex AI) + Preemptible VMs
→ AI Platform with Preemptible VMs: Low-cost compute resources for training large-scale models
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