End-to-End Capacity Planning for Modern Applications (Cloud, Microservices & DevOps) – Live Training
This Enterprise Capacity Planning & Performance Engineering Masterclass provides a practical, end-to-end approach to designing scalable, high-performance systems. The course covers workload modeling, demand forecasting, application, database, infrastructure, and cloud capacity planning using real-world enterprise scenarios. Learners will understand how to prevent outages, optimize resource utilization, and plan for peak traffic events. You will gain hands-on knowledge of performance testing, observability, SRE practices, and cloud auto-scaling strategies. This program is ideal for building scalable, resilient, and cost-efficient IT systems in modern DevOps and cloud-native environments.
About the Instructor:
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Sushant is a seasoned performance engineering and capacity planning professional with over 12 years of industry experience working on enterprise-scale applications. His expertise includes capacity forecasting, workload modeling, infrastructure sizing, and performance risk analysis across large production systems. He has supported business-critical platforms by aligning technical capacity strategies with growth and cost-optimization goals. His real-world exposure enables him to approach capacity planning as both a technical and business-driven discipline. With 4+ years of teaching experience, Sushant has successfully trained over 300 professionals through classroom and corporate training programs. Teaching is his passion, and he is known for his structured explanations, real-time industry examples, and learner-centric approach. He focuses on building strong fundamentals while enabling participants to apply concepts confidently in real projects. At OOR Institute, he is committed to developing industry-ready capacity planning professionals. |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 229 USD 179 119 USD Or USD1900 INR 15000 INR 9900 Rupees.
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Free Demo Session:
2nd March @ 9:00 PM – 10:00 PM (IST) (Indian Timings)
2nd March @ 10:30 AM – 11:30 AM (EST) (U.S Timings)
2nd March @ 3:30 PM – 4:30 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 9:00 PM – 10:00 PM (IST)
For Participants in the US: Monday to Friday @ 10:30 AM – 11:30 AM (EST)
For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)
What student’s have to say about Sushant:
| This course is fantastic for beginners! I had no prior experience with Python or Pandas, but now I feel confident in my ability to clean, transform, and analyze data. – Kanthi
I highly recommend this course to anyone looking to upskill in data analysis. The content is well-structured, and the pacing is just right. I particularly enjoyed the sections on exploratory data analysis and data visualization with Pandas. – Shyam This course exceeded my expectations! The instructors are knowledgeable and engaging, and the course material is presented in a clear and concise manner. I appreciated the emphasis on hands-on learning, which allowed me to immediately apply what I learned to real-world datasets. – Mounika |
Who can enroll for this course?
- Software Developers working on cloud or microservices applications
- DevOps Engineers managing CI/CD pipelines and production deployments
- Site Reliability Engineers (SREs) handling system scalability and uptime
- Performance Test Engineers working on load and stress testing
- Cloud Engineers managing AWS, Azure, or GCP environments
- System Administrators handling infrastructure and server capacity
- Technical Leads and Architects designing scalable systems
- IT Operations Professionals responsible for resource optimization
- QA Engineers interested in performance and scalability testing
- Freshers or IT professionals who want to build a career in Cloud & DevOps capacity planning
What will I Learn by end of this course?
- Understand capacity planning fundamentals and why it is critical for scalable systems
- Perform workload analysis and demand forecasting using real business data
- Design application-level capacity plans for monoliths and microservices
- Plan database capacity including growth, performance, and high availability
- Estimate and optimize infrastructure and network capacity
- Implement cloud capacity planning and auto-scaling strategies
- Use performance testing to validate capacity limits and breakpoints
- Identify bottlenecks across application, database, and infrastructure layers
- Apply SRE and observability metrics for continuous capacity planning
- Optimize cost, performance, and scalability in enterprise environments
Salient Features:
- 30 Hours of Live Training
- Every session gets recorded and lifetime access to these videos will be given.
- Course Completion Certificate
Course syllabus:
MODULE 1: Foundations of Capacity Planning (Sessions 1–5)
Session 1: Capacity Planning – Why It Makes or Breaks Systems
- What capacity planning really means (beyond infra sizing)
- Real outage case studies (Banking, eCommerce, Payments)
- Capacity planning vs performance testing
- Where most organizations fail
Session 2: Capacity Planning Lifecycle
- Demand → Modeling → Validation → Monitoring → Optimization
- Short-term vs long-term planning
- Reactive vs proactive planning
Session 3: Core Terminologies & Metrics
- Throughput, latency, concurrency
- Peak vs average load
- Headroom, saturation, utilization
- Little’s Law (practical explanation)
Session 4: Types of Capacity Planning
- Business capacity planning
- Service capacity planning
- Resource capacity planning
- Component-level planning
Session 5: Capacity Planning in SDLC
- Agile, DevOps & CI/CD alignment
- Shift-left capacity planning
- Role of PE, SRE, Architects
MODULE 2: Workload & Demand Analysis (Sessions 6–9)
Session 6: Understanding Business Demand
- Business events driving load
- Seasonal vs event-based traffic
- BFSI & FinTech traffic patterns
Session 7: User Behavior & Workload Modeling
- Think time, arrival rates
- User mix modeling
- Transaction criticality mapping
Session 8: Forecasting Techniques
- Linear growth
- Seasonal growth
- Event-based surge modeling
- Historical data analysis
Session 9: Creating Demand Forecast Models (Hands-on)
- Excel-based forecasting
- CAGR & growth assumptions
- Risk buffers
MODULE 3: Application-Level Capacity Planning (Sessions 10–14)
Session 10: Application Architecture Deep Dive
- Monolith vs Microservices
- Synchronous vs asynchronous systems
- Stateless vs stateful services
Session 11: App Server Capacity Planning
- JVM sizing fundamentals
- Thread pools, connection pools
- Memory & GC impact
Session 12: API & Microservices Capacity Planning
- Per-service throughput modeling
- Downstream dependency impact
- Fan-out & cascading failures
Session 13: Caching & Performance Patterns
- Cache hit ratio impact
- CDN, Redis, in-memory caches
- Capacity savings through caching
Session 14: App-Level Bottleneck Identification
- CPU vs Memory vs Threads
- Vertical vs horizontal scaling decisions
MODULE 4: Database Capacity Planning (Sessions 15–19)
Session 15: Database Workload Characteristics
- Read vs write intensive systems
- OLTP vs OLAP
- Transaction complexity
Session 16: DB Sizing & Growth Planning
- Storage growth forecasting
- Index growth
- Archival strategies
Session 17: DB Performance Metrics
- TPS, QPS
- Locking & contention
- Connection limits
Session 18: HA, DR & Replication Impact
- Active-active vs active-passive
- Replication lag
- Read replicas planning
Session 19: DB Bottleneck Simulation
- What breaks first and why
- Capacity buffers for peak events
MODULE 5: Infrastructure & Network Capacity Planning (Sessions 20–23)
Session 20: CPU, Memory & Disk Planning
- Utilization thresholds
- IO wait & disk latency
- Overcommitment risks
Session 21: Network Capacity Planning
- Bandwidth estimation
- Latency & packet loss
- East-west vs north-south traffic
Session 22: Load Balancers & Gateways
- L7 vs L4 capacity
- SSL termination impact
- Failover scenarios
Session 23: Capacity Planning for HA Systems
- N+1, N+2 models
- Failure scenarios
- Degraded mode planning
MODULE 6: Cloud Capacity Planning (Sessions 24–28)
Session 24: Cloud Capacity Planning Fundamentals
- Cloud is not infinite
- Quotas, limits, throttling
Session 25: Auto-scaling Strategies
- Horizontal vs vertical scaling
- Scaling policies
- Cold start impact
Session 26: Cost vs Performance Trade-offs
- Right sizing
- Over-provisioning vs under-provisioning
- Reserved vs on-demand
Session 27: Container & Kubernetes Capacity Planning
- Pod resource requests & limits
- Node sizing
- Cluster autoscaler logic
Session 28: Cloud Failure Case Studies
- When auto-scaling fails
- Misconfigured limits
- Cost explosions
MODULE 7: Validation, Monitoring & Optimization (Sessions 29–32)
Session 29: Capacity Validation via Performance Testing
- How to design capacity-focused tests
- Breaking points vs safe limits
Session 30: Observability for Capacity Planning
- Golden signals
- Trend analysis
- Leading vs lagging indicators
Session 31: Continuous Capacity Planning
- Feedback loops
- Monitoring-driven forecasting
- Capacity debt concept
Session 32: Optimization Techniques
- App tuning
- DB tuning
- Infra & cloud optimization