professional Diploma in Mastering Scalable Web Applications

The Mastering Scalable Web Applications course offered by Geneve Institute of Business Management offers a concentrated, practitioner-focused programme that equips engineers and technical leaders with the concepts and judgement needed to design, build and operate web systems that serve growing traffic reliably. Over ten instructional units the syllabus covers architecture patterns, performance engineering, operational observability, and organisational practices that influence scalability. Emphasis rests on clear mental models—how state, concurrency, networking and storage interact under load—so participants can make defensible trade-offs across cost, latency and resilience. Learners leave with a catalogue of proven patterns and criteria to evaluate technology choices, plan capacity, and shape teams for sustained service growth.

Target group

• Backend engineers responsible for designing and implementing services that must scale predictably under heavy load.

• Site reliability engineers and operations staff charged with keeping distributed web systems available and performant.

• Technical architects and engineering managers who define system boundaries, capacity budgets, and platform roadmaps.

• DevOps practitioners building CI/CD pipelines, automated deployments and runtime observability for production services.

• Platform and infrastructure engineers selecting databases, caches, message buses and networking fabrics for scalable applications.

• Technical leads and product engineers aiming to translate scalability requirements into concrete design decisions and priorities.

Objectives

• Explain core scalability drivers: concurrency, resource contention, network behaviour and data placement trade-offs.

• Choose appropriate architectural patterns—layered, microservices, event-driven, or serverless—based on workload characteristics.

• Design capacity and performance strategies including caching, load distribution, sharding, and graceful degradation techniques.

• Implement operational observability: metrics, logging, tracing and alerting that reveal system health under stress.

• Plan deployment and release strategies that reduce blast radius, enable quick rollbacks and support continuous delivery.

• Establish team processes and platform capabilities for predictable scaling, incident response and ongoing performance improvement.

Course Outline

• System Design Foundations:

  • Defining scalability goals: throughput, latency targets and availability SLAs.

  • Workload classification: CPU-bound, I/O-bound, memory-bound and mixed patterns.

  • Capacity modelling basics: demand forecasting and headroom planning.

  • Trade-off framing: cost, complexity and operational overhead considerations.

  Architectural Patterns Overview:

  • Monoliths, modular monoliths and microservices: benefits and costs.

  • Service-oriented versus event-driven designs and their scaling implications.

  • Serverless and managed services trade-offs for elasticity and control.

  • Choosing databases and storage patterns aligned with access characteristics.

• Networking and Load Distribution:

  • Load balancing strategies: DNS, L4/L7, and global traffic management.

  • Connection management, keepalive, and proxy implications for scale.

  • Network partitioning and regional deployment patterns for latency reduction.

  • Traffic shaping, throttling and backpressure controls at the edge.

  API Design for Scale:

  • Contract design: pagination, filtering, idempotency and versioning for robust APIs.

  • Request sizing, batching and avoiding chatty protocols.

  • Rate limiting strategies and fair-usage algorithms.

  • API gateways, edge caching and request routing policies.

• Caching and State Management:

  • Multi-layer caching: CDN, edge, application and in-process caches benefits.

  • Cache invalidation strategies and consistency trade-offs.

  • Session management, sticky sessions and stateless alternatives.

  • When to use in-memory stores versus persistent caches and their failure modes.

  Data Partitioning and Storage:

  • Horizontal partitioning (sharding) strategies and key design considerations.

  • Denormalisation, materialised views and read-optimised schemas.

  • Choosing SQL, NoSQL or NewSQL based on consistency and query needs.

  • Backup, restore and cross-region replication patterns for data durability.

• Asynchronous Processing and Messaging:

  • Queueing patterns, durable tasks and at-least-once versus exactly-once semantics.

  • Event-driven pipelines, event sourcing and temporal considerations.

  • Selecting brokers and configuring retention, replication and consumer groups.

  • Handling retries, dead-letter queues and long-tail processing.

  Concurrency and Parallelism:

  • Threading, actors and process models for concurrent request handling.

  • Work partitioning, fan-out/fan-in and coordination costs.

  • Synchronisation primitives, contention hotspots and mitigation techniques.

  • Bulkheads, rate limitations and isolation to contain failures.

• Performance Engineering:

  • Profiling at scale: hotspot identification in services and databases.

  • Load testing principles, representative scenarios and result interpretation.

  • Latency budgets, SLO-driven development and prioritising improvements.

  • Cost-performance trade-offs and optimisation prioritisation heuristics.

  Resource Management and Cost Control:

  • Autoscaling strategies, cool-down windows and stability implications.

  • Right-sizing instances, instance families and mixed tenancy models.

  • Resource quotas, reservation and overcommit strategies.

  • Monitoring spend, forecasting, and cost-based decision criteria.

• Observability and Diagnostics:

  • Defining key metrics: throughput, error rates, latency percentiles and saturation signals.

  • Structured logging practices and log retention policies for post-incident analysis.

  • Distributed tracing to follow requests across service boundaries effectively.

  • Alerting design: signal-to-noise, escalation paths and runbook integration.

  Incident Management and Resilience Testing:

  • Incident lifecycle: detection, mitigation, post-mortem and action tracking.

  • Chaos engineering principles and controlled failure injection approaches.

  • Recovery objectives: RTO, RPO and practical restoration playbooks.

  • Communication protocols and stakeholder coordination during service disruption.

• Security and Compliance at Scale:

  • Authentication and authorization models suitable for high-throughput services.

  • Secrets management, key rotation and least-privilege deployment patterns.

  • Protecting against volumetric attacks, abuse and rate-based exploits.

  • Compliance implications for data residency, audit logs and access controls.

  Observing and Protecting Data:

  • Encryption in transit and at rest, and key-management trade-offs.

  • Data minimisation, retention policies and privacy by design approaches.

  • Detecting anomalous data access patterns and protecting sensitive endpoints.

  • Secure supply-chain practices for dependencies and deployment artifacts.

• Deployment Models and Release Management:

  • Blue-green, canary and incremental rollout strategies and rollback mechanisms.

  • Immutable infrastructure, image-based deployments and declarative infrastructure as code.

  • Orchestration choices: containers, serverless, and managed platform trade-offs.

  • Release gating, automated verification and progressive delivery checks.

  CI/CD and Platform Automation:

  • Pipeline design for reproducible builds, tests and promotion flows.

  • Environment parity, configuration management and secrets handling in pipelines.

  • Automated rollback, health-check gating and post-deploy validation.

  • Self-service platform features that enable developer velocity while maintaining safety.

• Scalability for Databases and Storage Systems:

  • Scaling relational databases: read-replicas, partitioning and workload routing.

  • NoSQL scaling semantics, consistency models and query trade-offs.

  • Object storage patterns for large binary artefacts and cold data strategies.

  • Data lifecycle, tiering, and cost-optimised archival approaches.

  Cross-Region and Multi-Cloud Strategies:

  • Designing for region failover, active-active and active-passive topologies.

  • Data replication, conflict resolution and eventual consistency implications.

  • Networking considerations: inter-region latency, egress costs and routing policies.

  • Evaluating multi-cloud complexity versus resilience and vendor independence.

• Organisational Practices for Scalability:

  • Team structures that influence service ownership and operational accountability.

  • SLO-driven prioritisation, runbooks and continual improvement cycles.

  • Capacity reviews, incident retrospectives and long-term remediation tracking.

  • Developer education, playbooks and knowledge transfer to maintain standards.

  Observability-Driven Development and Culture:

  • Embedding metrics and tracing into feature development workflows.

  • Measuring impact of changes against SLIs and iterating on results.

  • Shared dashboards, alerting ownership and cross-team correlation practices.

  • Incentivising reliability by aligning goals, rewards and recognition.

• Emerging Technologies and Future-Proofing:

  • Edge computing, eBPF, and emerging networking trends that affect scaling choices.

  • Advances in storage engines, distributed consensus and database architectures.

  • Observability standards, service meshes and platform abstractions shaping operations.

  • Evaluating new tech responsibly: pilot patterns and measurable adoption criteria.

  Career Pathways and Continued Mastery:

  • Roles enabled by this skill set: SRE, platform architect, scalability engineer.

  • Building a professional portfolio: systems you’ve scaled and measurable outcomes.

  • Learning resources, conferences and communities focused on web-scale engineering.

  • Roadmaps for specialisation: performance, security, platform or data-centric tracks.