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Performance engineers correlating workload latency throughput errors and infrastructure signals
SQA, Test Automation & SDET · Course #036

Performance Engineering & Load Testing

In modern delivery environments, performance engineering & load testing must support fast feedback without sacrificing evidence or control. This course enables learners to connect workload, system architecture, resource behaviour and service objectives to a repeatable performance-engineering process, working through performance engineering lifecycle and system context, slis, slos, thresholds and acceptance risks and workload modelling from user and transaction demand. Learners define scenarios and SLIs, design staged tests, monitor application and infrastructure signals, correlate bottlenecks across tiers and communicate capacity or resilience findings with explicit limitations. Exercises require clear assumptions, useful failure messages and proportionate review. The emphasis moves from generating traffic to forming and testing credible hypotheses about system behaviour under expected and extreme conditions.

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Overview

In modern delivery environments, performance engineering & load testing must support fast feedback without sacrificing evidence or control. This course enables learners to connect workload, system architecture, resource behaviour and service objectives to a repeatable performance-engineering process, working through performance engineering lifecycle and system context, slis, slos, thresholds and acceptance risks and workload modelling from user and transaction demand. Learners define scenarios and SLIs, design staged tests, monitor application and infrastructure signals, correlate bottlenecks across tiers and communicate capacity or resilience findings with explicit limitations. Exercises require clear assumptions, useful failure messages and proportionate review. The emphasis moves from generating traffic to forming and testing credible hypotheses about system behaviour under expected and extreme conditions.

Course Highlights

Service-level objectives and indicators

Production-informed workload models

Staged load, stress and endurance tests

Application and infrastructure observability

Bottleneck hypothesis testing

Modules & Curriculum

Performance engineering lifecycle and system context

SLIs, SLOs, thresholds and acceptance risks

Workload modelling from user and transaction demand

Baseline, load, stress, spike and endurance designs

Test-environment fidelity and measurement bias; observability: client, service, database and infrastructure signals

Bottleneck analysis, queuing and saturation patterns

Distributed load and test-controller considerations

Capacity, scalability and resilience experiments; evidence-led report and remediation verification

Learning Outcomes

  • Translate business demand into measurable performance objectives.
  • Build a representative workload and staged test model.
  • Select client, application and infrastructure signals for observation.
  • Correlate latency and errors with resource and dependency behaviour.
  • Differentiate bottlenecks, saturation, queuing and capacity limits.

Ready to Build Your Future-Ready Skills?

Move beyond traffic generation to explain bottlenecks, capacity and performance risk.

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