Results for “capacity-planning”

Guides scaling decisions for Qdrant vector databases based on data volume, query throughput, latency, or query volume.

Almost every cloud estate is over-provisioned.

Two numbers with two different audiences.

Plans and configures multi-tenancy on GKE, covering namespace isolation, RBAC planning, resource quotas, LimitRanges, network isolation, and cost allocation.

Optimize Kubernetes cluster costs by right-sizing resources, configuring autoscalers, using spot instances, and monitoring spend.

Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues.

Layer 6 of the keyword research pipeline. Organizes the vetted keyword queue into money clusters (from clusters.md), picks each cluster's keystone vs supporting articles, tracks coverage, and PROPOSES new clusters from high-business-value topics or live products not yet covered — so the blog strategy expands as the company grows. Emits cluster-tagged queue + cluster-map.md + cluster-proposals.md.

Evaluates a containerized framework for deploying distributed big data workloads, measuring execution time and cloud cost scaling from four to eight nodes.

Provides deployment strategies, CI/CD pipeline patterns, Docker containerization best practices, health checks, and production readiness guidance for web applications.

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.

Compute, object storage, block storage, a managed relational database, a message

Kubernetes

Optimizing FinOps and cloud cost: IaC-based estimation, right-sizing, RI/SP recommendations, anomaly detection, budget alerts, AI/GPU workload economics. Use to forecast or cut cloud spend.

Use this when planning environment variables, config files, secrets, flags, and provider settings across local, CI, staging, production, serverless, or multi-runtime systems.

Configures GKE workload reliability with PodDisruptionBudgets, health probes, topology spread constraints, and graceful shutdown patterns.

Wraps scheduled task execution with Anthropic OAuth usage limit awareness, pausing when thresholds are exceeded.

Assess and manage Azure service quotas and limits to prevent deployment and scaling failures. Use this skill when users ask about quota errors, capacity planning, regional availability, or resource limits.

Designs and executes load tests, covering scenario design, baseline capture, execution configuration, results analysis, and reporting for k6, Locust, Gatling, and JMeter.

Avalia a confiabilidade de uma plataforma inteira, mapeando pontos únicos de falha, calculando error budgets a partir de SLOs, revisando prontidão para produção e analisando resiliência e capacidade.

Build production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "timeout strategy", "deployment pipeline", "chaos engineering", "bulkhead pattern", "retry with backoff", or "health checks". Also trigger when designing resilient microservices, planning zero-downtime deployments, or investigating cascading failure scenarios. Covers capacity planning, health checks, and anti-fragility patterns. For data systems, see ddia-systems. For system architecture, see system-design.