Cloud computing gives teams ways to run software and manage data without operating every piece of infrastructure themselves. But choosing services is only part of the work: engineers also need to plan deployments, networking, security, reliability, and costs as systems grow.

This guide is organised from fundamentals to advanced topics, so you can start with cloud models and deployment before moving into platform engineering, Kubernetes, distributed systems, and edge computing. It is written for working software engineers and tech leads, and is updated as new articles are published. Use the sections to build a practical foundation or jump to the challenges most relevant to your systems.

What this guide covers

• Cloud computing fundamentals and deployment
• Cloud operations, networking, and security
• Kubernetes and platform engineering
• Distributed systems and reliability
• Edge computing and runtimes
• Cloud data and application services

Cloud computing fundamentals and deployment

Start with cloud service models, platform choices, deployment practices, and the broader trends shaping cloud adoption.

• Demystifying Cloud Computing: A Comprehensive Guide – Introduces IaaS, PaaS, and SaaS alongside cloud storage, scaling, costs, and disaster recovery.
• SaaS Adoption: Benefits and Challenges for Modern Businesses – Summarizes SaaS benefits and challenges, including provider-managed updates, internet dependence, and vendor lock-in.
• Top Cloud Computing Trends in 2024 – Highlights AI as a Service, edge computing, and Zero Trust among cloud trends in 2024.
• GCP vs. AWS vs. Azure: Cloud Platform Comparison – Compares cloud platforms and highlights GCP services for analytics, hybrid workloads, and usage discounts.
• Google Cloud Platform: Benefits and Key Insights – Covers BigQuery, Anthos, and discounts for GCP analytics and hybrid deployments.
• Cloud Deployment: What Happens When You Deploy? – Explains how CI/CD, infrastructure as code, and runtime configuration move code into production.

Cloud operations, networking, and security

Explore operational practices, cloud networking, access controls, and cost-aware safeguards for running services.

• Mastering IT Infrastructure Management: Key Strategies – Recommends infrastructure audits, automation, and monitoring to improve reliability and resource use.
• Enterprise IT Solutions: Transforming Your Business Future – Covers automation, system integration, and cloud scalability for complex enterprise IT needs.
• GCP Networking Solutions for Reliable Connectivity – Outlines GCP VPC design, firewall rules, VPN or Interconnect links, and load balancing.
• GCP Networking: A Deep Dive – Covers GCP subnets and firewall rules, on-premises connectivity, and network performance monitoring.
• GCP Network Intelligence Center: Transform Network Management – Describes Connectivity Tests and Network Topology for diagnosing reachability and network bottlenecks in GCP.
• GCP Network Intelligence Center: Network Performance Guide – Covers GCP Connectivity Tests, Performance Dashboard metrics, and Firewall Insights for network troubleshooting.
• Implementing Zero Trust Security in an AWS Environment – Covers AWS Zero Trust practices including MFA, least-privilege IAM, and continuous monitoring.
• AWS Zero Trust Security: Principles and Best Practices – Describes AWS Zero Trust controls such as MFA, least-privilege policies, network segmentation, and monitoring.
• Cloud FinOps, Zero-Trust Egress, and Edge Fallback – Discusses eBPF egress monitoring and budget-aware S3 circuit breakers for runaway serverless costs.

Kubernetes and platform engineering

Learn how teams manage Kubernetes fleets, platform control planes, service boundaries, and resilient application patterns.

• Platform Engineering: Controlling Agent Sprawl and Micro-Clouds – Describes a multi-tenant control plane, cloud discovery, and token gateways for managing agent sprawl.
• Kubernetes Control Plane Failures in Modern Cloud Systems – Connects etcd latency and watch storms to Kubernetes control-plane strain and discusses isolation guardrails.
• Kubernetes Multi-Cluster Challenges for Platform Teams – Covers multi-cluster drift, recovery costs, GitOps, and hub-and-spoke governance for Kubernetes fleets.
• Unlocking the Advantages of Microservices Architecture – Describes independent scaling, fault isolation, and faster releases as microservices architecture benefits.
• Ghost Architecture: The Hidden Layer for Fast, Scalable Software – Describes retries, circuit breakers, caching, and graceful fallbacks for handling traffic spikes and failures.

Distributed systems and reliability

Understand failure modes in distributed workloads and approaches for containing outages and protecting data consistency.

• Kafka: Why Real-Time Platforms Collapse at Scale – Explains how Kafka replay storms, hot partitions, and consumer lag can contribute to scale failures.
• Event-Driven Architecture: Truths and When Not to Use It – Examines event-driven architecture’s coupling, consistency, and debugging challenges, with guidance on tracing and idempotency.
• Distributed Systems: Why They Fail and How to Prevent It – Covers bulkheads, jittered retries, idempotency, and load shedding to contain distributed-system failures.
• Distributed Consensus: Fixing Server-Side State at Scale – Explains how Raft quorum commits, leader leases, and jittered backoff can reduce consensus-related risks.
• Distributed Data Consistency: Why Systems Fail at Scale – Discusses sagas and idempotency as ways to address duplicate messages and partial failures.
• Cloud Region Failure: Systems That Survive a 2AM Outage – Covers multi-region failover, data replication, and failure containment for cloud-region outages.
• eBPF Zero-Trust Egress and Multi-Cloud Failover – Discusses eBPF egress controls and circuit breakers for handling NAT connection drops and cloud failover.

Edge computing and runtimes

Compare cloud, edge, and fog processing, then consider redundancy and newer runtime approaches for local workloads.

• Distributed Computing: From Cloud to Edge to Fog – Explains how edge processing and fog nodes support local decisions and IoT data aggregation.
• Understanding Edge Computing: Future of Software Architecture – Explains how local processing, edge gateways, and resilient nodes address latency and bandwidth limits.
• Understanding Redundancy in Edge Computing – Covers edge node redundancy, data replication, and tested failover strategies for reducing downtime.
• Wasm & eBPF Micro-Kernels After the Container Revolution – Covers WASI 0.2 components and eBPF XDP for isolated compute and fast packet handling.

Cloud data and application services

See how cloud services support analytics, AI, language applications, robotics, and customer relationship management.

• Modern Data Analytics: Reference Architecture Simplified – Outlines batch or streaming ingestion, lake-and-warehouse storage, and governance for analytics systems.
• DataBricks: Empowering Data Analytics at Scale – Introduces Databricks with Apache Spark and Delta Lake, including performance, security, and cost considerations.
• Why GCP is Perfect for AI Development and Innovation – Covers GCP AI development with BigQuery ML and TPUs, plus IAM, encryption, and audit logging.
• Power of Natural Language Processing with AWS – Covers AWS Comprehend sentiment analysis and language services for speech, translation, and chat.
• Empowering Language-Driven Applications with AWS NLP – Covers customer-review sentiment analysis with Comprehend and deployment using Lambda and API Gateway.
• Revolutionizing Robotics: AWS RoboMaker Unleashed – Explains RoboMaker simulations, ROS development, fleet deployment, OTA updates, and monitoring.
• AWS RoboMaker: A Deep Dive Into Robotics Development – Covers RoboMaker virtual scenarios, ROS-based development, and cloud deployment to robot fleets.
• A Deep Dive into Salesforce, the Pinnacle of CRM Excellence – Describes Salesforce tools for lead tracking, follow-ups, customer analytics, and personalized marketing.
• Salesforce: Customer Engagement and Business Transformation – Explains how Salesforce can automate follow-ups and use customer data for personalized marketing.

Frequently asked questions

What is cloud computing?

Cloud computing provides on-demand access to computing resources such as servers, storage, databases, and networking. Organizations can use these resources through a provider rather than buying and maintaining all hardware themselves. Services are commonly grouped as infrastructure, platform, or software as a service, with different levels of management and control.

When should a team use Kubernetes?

Kubernetes can help teams deploy and operate containerized applications across a cluster, especially when they need consistent scheduling, service discovery, and automated recovery. It also adds operational complexity. A team should consider its workload, reliability needs, skills, and maintenance capacity before adopting it; simpler deployment options may be a better fit.

What is platform engineering?

Platform engineering is the practice of building and maintaining shared tools, workflows, and services that help software teams deliver and operate applications. A platform may provide deployment paths, infrastructure automation, security controls, or observability. Its purpose is to make common engineering tasks more consistent while allowing teams to retain appropriate ownership and flexibility.

Keep learning

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