Google Cloud Platform
☁️ Introduction
Google Cloud Platform (GCP) offers a comprehensive suite of cloud computing services for data engineering, machine learning, and application development. This hub explores practical patterns, best practices, and real-world implementations on GCP.
🔧 Core services
Data & analytics
- BigQuery: Serverless data warehouse for analytics at scale
- Dataproc: Managed Apache Spark and Hadoop clusters
- Dataflow: Stream and batch data processing with Apache Beam
- Cloud Storage: Object storage for data lakes and archives
- Pub/Sub: Real-time messaging for event-driven architectures
AI & machine learning
- Vertex AI: Unified ML platform for model development and deployment
- BigQuery ML: SQL-based machine learning workflows
- AutoML: No-code machine learning model training
- AI Platform: Custom ML training and serving infrastructure
- Document AI: Intelligent document processing
Compute & infrastructure
- Compute Engine: Virtual machines for custom workloads
- Google Kubernetes Engine (GKE): Managed Kubernetes clusters
- Cloud Functions: Serverless compute for event-driven functions
- Cloud Run: Containerized applications with automatic scaling
- App Engine: Platform-as-a-Service for web applications
Developer tools
- Cloud Build: CI/CD pipelines and build automation
- Cloud Source Repositories: Git-based source control
- Container Registry: Docker image management
- Cloud SDK: Command-line tools and libraries
- Terraform: Infrastructure as Code for GCP resources
Featured articles
Below are the latest articles tagged with “gcp”:
Slicing time at scale: Building a Scala SDK for petabyte CDC on GCP at scale
Deep-dive into building a Scala SDK for CDC at petabyte scale on GCP. Learn partition pruning, getAffectedPartitions, cloud-native backups, and CDC without ACID. Includes metrics, scan reduction stats, and code from Delta/Hive lakes.
My road to Google Developer Expert (GDE): DevFest samosas, OSS PRs, acceptance
Breakdown of my GDE acceptance journey. Learn contribution tracking, referral tactics, interview focus areas, OSS PR examples, blogging setup, CFP submissions, and mentorship. Includes the spreadsheet system and interview questions.
Functional utilities for data engineering: stop repeats in Scala pipelines now
Build reusable Scala utilities for data engineering on GCP, covering CDC, Delta maintenance, schema evolution, GCS helpers, affected-partition strategies, and error handling. Includes patterns for safe ops and repeatable pipelines.
Cloud-native AI workflows: BigQuery ML + Vertex AI (skip the CSV exports) now
Guide to building AI pipelines with BigQuery ML and Vertex AI. Learn SQL-first modeling, real-time endpoints, automated retraining, and drift monitoring. Includes diagrams and code that cut time-to-model from 6 weeks to 2 hours fast.
Forecasting at scale: demand prediction with Random Forests and neural nets
Building a demand forecasting system with Random Forest, ANN, and hybrid time-series models on Hadoop and Spark. Covers data ingestion, feature engineering, model training, validation, and rollout for large SKU catalogs and seasonality.
Architecture patterns
Data engineering
- Data lake architecture: Building scalable data platforms
- ETL/ELT pipelines: Batch and streaming data processing
- Data warehousing: Modern analytics with BigQuery
- Real-time analytics: Streaming data with Pub/Sub and Dataflow
- Data governance: Security, compliance, and data quality
Machine learning
- ML pipelines: End-to-end model development workflows
- Feature stores: Managing ML features at scale
- Model serving: Real-time and batch prediction serving
- MLOps: Continuous integration for machine learning
- AutoML workflows: Democratizing machine learning
Application development
- Microservices: Container-native application architecture
- Event-driven: Building reactive systems with Pub/Sub
- API development: RESTful and GraphQL APIs on GCP
- Static sites: JAMstack deployments with Cloud Storage
- Multi-region: Global application deployment patterns
Best practices
Security & compliance
- Identity & Access Management (IAM): Fine-grained permissions
- Service accounts: Secure service-to-service authentication
- VPC security: Network isolation and firewall rules
- Data encryption: At-rest and in-transit encryption
- Audit logging: Comprehensive security monitoring
Cost optimization
- Resource right-sizing: Optimizing compute and storage
- Committed use discounts: Long-term cost savings
- Preemptible instances: Cost-effective batch processing
- BigQuery optimization: Query and storage cost reduction
- Monitoring & alerting: Proactive cost management
Performance & reliability
- Auto-scaling: Dynamic resource allocation
- Load balancing: High-availability application design
- Disaster recovery: Business continuity planning
- Monitoring: Comprehensive observability with Cloud Operations
- SLA management: Meeting service level objectives
Related topics
- Data engineering - Building data systems on GCP
- Apache Spark - Running Spark on Dataproc
- Scala - Developing GCP applications with Scala
- Open source - Contributing to GCP-related projects
Building on Google Cloud? Get in touch or explore the articles above for proven patterns and optimization strategies.