Software Architecture Patterns for Big Data

This course is part of Software Architecture for Big Data Specialization

Instructors: Tyson Gern +1 more

What you'll learn

  •   Compare, measure, and test big data models for production use.
  •   Write custom performance tests to measure the characteristics of a distributed system.
  •   Use queues to horizontally distribute large workloads.
  • Skills you'll gain

  •   Software Architecture
  •   Data Architecture
  •   Test Automation
  •   Distributed Computing
  •   Performance Testing
  •   Unit Testing
  •   Scalability
  •   Application Performance Management
  •   Predictive Modeling
  •   Database Architecture and Administration
  • There are 4 modules in this course

    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. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

    Performance of Distributed Systems

    Horizontal Distribution of Large Workloads

    Highly Available Distributed Systems

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