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Google Cloud Platform (GCP)

Master Google's comprehensive cloud platform with AI/ML focus and data analytics excellence

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What is Google Cloud Platform?

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google that runs on the same infrastructure that Google uses internally for its end-user products like Google Search, Gmail, and YouTube. GCP provides computing, data storage, data analytics, and machine learning services with a strong focus on artificial intelligence and data analytics.

Launched in 2008, GCP has grown to become a major player in the cloud computing market, known for its advanced AI/ML capabilities, data analytics tools, and Google's expertise in handling massive scale. GCP serves millions of developers and enterprises worldwide with services spanning compute, storage, databases, networking, AI/ML, and developer tools.

GCP Cost Calculator

n1-standard-1 (1 vCPU, 3.75GB RAM)

$121
Compute Engine
$10
Cloud Storage
$25
BigQuery
$1
Vertex AI

Total Monthly Cost: $157

Estimates based on US pricing • Query latency: ~3s

GCP Core Services

Compute Engine

Scalable virtual machines running on Google's infrastructure with custom machine types.

• Custom machine types
• Preemptible instances
• Managed instance groups
• Live migration
• Sustained use discounts

BigQuery

Serverless data warehouse for analytics with built-in machine learning capabilities.

• Petabyte-scale analytics
• Standard SQL support
• Built-in ML (BQML)
• Real-time streaming
• Federated queries

Vertex AI

Unified machine learning platform for building, deploying, and scaling ML models.

• AutoML capabilities
• Custom training
• Model deployment
• MLOps workflows
• Feature Store

Google Kubernetes Engine

Managed Kubernetes service with Google's container orchestration expertise.

• Autopilot mode
• Cluster autoscaling
• Workload Identity
• Binary Authorization
• Istio service mesh

GCP AI/ML Services

Pre-trained APIs

Ready-to-use AI services for common tasks.

  • • Vision API (image analysis)
  • • Natural Language API
  • • Translation API
  • • Speech-to-Text API
  • • Video Intelligence API

AutoML

Build custom ML models with minimal coding.

  • • AutoML Vision
  • • AutoML Natural Language
  • • AutoML Translation
  • • AutoML Tables
  • • AutoML Video Intelligence

Custom Training

Build and train custom models at scale.

  • • TensorFlow support
  • • PyTorch support
  • • Scikit-learn support
  • • Distributed training
  • • Hyperparameter tuning

GCP Data Services

Cloud Storage

Object storage with multiple storage classes for different use cases.

• Standard storage
• Nearline storage
• Coldline storage
• Archive storage
• Multi-regional replication

Cloud SQL

Fully managed relational database service for MySQL, PostgreSQL, and SQL Server.

• Automated backups
• High availability
• Read replicas
• Point-in-time recovery
• Automatic scaling

Cloud Spanner

Globally distributed, strongly consistent database service.

• Global consistency
• Horizontal scaling
• 99.999% availability
• ACID transactions
• SQL interface

Firestore

NoSQL document database built for automatic scaling, high performance.

• Document model
• Real-time updates
• Offline support
• Multi-region replication
• ACID transactions

GCP Analytics Pipeline

Data Ingestion

Multiple ways to get data into Google Cloud for processing and analysis.

Cloud Pub/Sub → Cloud Dataflow → BigQuery
Cloud Storage → Cloud Dataprep → BigQuery
Cloud SQL → Cloud Data Fusion → BigQuery

Data Processing

Transform and process data at scale using Google's serverless technologies.

Apache Beam on Cloud Dataflow
// Apache Beam on Cloud Dataflow
pipeline = beam.Pipeline()
(pipeline | ReadFromPubSub(topic)
         | ProcessData()
         | WriteToBigQuery(table))

Data Analysis

Analyze data using BigQuery's powerful SQL engine and built-in ML capabilities.

BigQuery ML Analysis
SELECT customer_id, predicted_clv
FROM ML.PREDICT(MODEL `dataset.customer_ltv_model`,
  (SELECT * FROM `dataset.customer_features`))
ORDER BY predicted_clv DESC

Real-World GCP Implementations

Spotify

Uses GCP for data analytics and machine learning to power music recommendations.

  • • BigQuery for analytics
  • • Dataflow for processing
  • • TensorFlow for ML models
  • • Pub/Sub for real-time data

Snapchat

Leverages GCP's AI services for augmented reality filters and content analysis.

  • • Vision API for AR filters
  • • Compute Engine for processing
  • • Cloud Storage for media
  • • BigQuery for analytics

The New York Times

Uses GCP for digital transformation and content delivery optimization.

  • • App Engine for websites
  • • Cloud CDN for delivery
  • • BigQuery for reader analytics
  • • AI for content recommendations

PayPal

Utilizes GCP for fraud detection and risk management using machine learning.

  • • Vertex AI for fraud detection
  • • BigQuery for transaction analysis
  • • Cloud Security for compliance
  • • Dataflow for real-time processing

GCP Best Practices

✅ Do

  • • Use IAM roles and service accounts properly
  • • Implement proper resource hierarchy (org/folders/projects)
  • • Use labels for cost management and organization
  • • Leverage committed use discounts for predictable workloads
  • • Use BigQuery partitioning and clustering
  • • Implement security best practices with VPC and firewall rules

❌ Don't

  • • Grant overly broad IAM permissions
  • • Ignore cost monitoring and budgets
  • • Store sensitive data without encryption
  • • Use default service accounts in production
  • • Forget to enable audit logging
  • • Ignore data residency and compliance requirements

📝 Google Cloud Platform Quiz

1 of 6Current: 0/6

What is Google Cloud Platform's data analytics warehouse service?