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University at Buffalo

UB Undergraduate Academic Schedule: Spring 2019

  • This information is updated nightly. Additional information about this course, including real-time course data, prerequisite and corequisite information, is available to current students via the HUB Student Center, which is accessible via MyUB. Information about HUB can be found at

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    CSE 487LR - Data Intensive Computing
    Data Intensive Computing R1 Enrollment Information (not real time - data refreshed nightly)
    Class #:   19733   Enrollment Capacity:   30
    Section:   R1   Enrollment Total:   28
    Credits:   4.00 credits   Seats Available:   2
    Dates:   01/28/2019 - 05/10/2019   Status:   OPEN
    Days, Time:   M , 8:00 AM - 8:50 AM
    Room:   Cooke 114 view map
    Location:   North Campus      
    Chained Courses
    Registering in the above section will automatically place you in the following class(es):
    Enrollment Requirements
    Prerequisites: Pre-Requisite: CSE 250 and approved Computer Science, Computer Engineering, Bioinformatics/CS Majors only. Departmental senior standing recommended.
      Course Description
    Data-intensive computing deals with storage models, application architectures, middleware, and programming models and tools for large-scale data analytics. In particular we study approaches that address challenges in managing and utilizing ultra-scale data and the methods for transforming voluminous data sets (big data) into discoveries and intelligence for human understanding and decision making. Topics include: storage requirements of big data, organization of big data repositories such as Google File System (GFS) semantic organization of data, data-intensive programming models such as MapReduce, fault-tolerance, privacy, security and performance, services-based cloud computing middleware, intelligence discovery methods, and scalable analytics and visualization. This course has three majors goals: (i) understand data-intensive computing, (ii) study, design and develop solutions using data-intensive computing models such as MapReduce and (iii) focus on methods for scalability using the cloud computing infrastructures such as Google App Engine (GAE), Amazon Elastic Compute Cloud (EC2), and Windows Azure. On completion of this course students will be able to analyze, design, and implement effective solutions for data-intensive applications with very large scale data sets.
      On-line Resources