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The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production.
You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course you will know how to scale production data stores to perform under load, designing load tests to ensure applications meet performance requirements.
Software Architecture Patterns for Big Data can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Overview
Syllabus
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- Predictive Models
- In this module, you will learn how to write tests that allow you to iterate on predictive models.
- Performance of Distributed Systems
- In this module, you will learn how to write performance tests to ensure your distributed system operates as expected in production.
- Horizontal Distribution of Large Workloads
- In this module, you will learn how to use queues to horizontally distribute large workloads.
- Highly Available Distributed Systems
- In this module, you will learn the advantages and disadvantages of high availability distributed systems.
