Software Architecture Patterns for Big Data

Colorado Boulder examines patterns for systems that process substantial data and workloads. Modules move from predictive models to distributed performance, horizontal distribution and availability. The published outcomes include custom performance tests and queues, giving the course a concrete evaluation focus.

What you will learn

• Measure performance characteristics of a distributed system.
• Use queues to distribute a workload horizontally.
• Compare models and availability choices for production use.

Who this suits

Advanced learners with software-engineering or big-data experience.

Before you enroll

Review the infrastructure and performance-testing setup before enrolling. Capacity claims depend on workload and environment, so reproduce measurements rather than assuming an example result transfers to your system.

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Put it into practice

Define a repeatable workload and a success metric. Compare one worker with several workers, documenting where queueing, coordination or a shared dependency limits improvement.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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