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ICS 411 - Big Data Storage and Processing
Fall 2019, Section 01

search actionsID #Subj#SecTitleDatesDaysTimeCrdsStatusInstructorDelivery MethodLoc
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001072 ICS 411 01 Big Data Storage and Processing
08/24 - 12/14
Sa
9:10am - 12:30pm
4.0 Open Armitage, Bradford
Ghanem, Thanaa
Location: z MnSCU Metropolitan State University
Building/Room: Founders Hall L117


Meeting Details
DatesDaysTimeBuilding/RoomInstructor
8/24/2019 - 12/14/2019 Sa 9:10am - 12:30pm Founders Hall L117 Armitage, Bradford
Ghanem, Thanaa

Notes
  • Note: Students are responsible to both be aware of and abide by prerequisites for ICS courses for which they enroll, and will be administratively dropped from a course if they have not met prerequisites.

Location Details
Offered through: Metropolitan State University.
Campus: Metropolitan State University. Location: z MnSCU Metropolitan State University.

Seat Availability
Status: Open Size: 28 Enrolled: 14 Seats Remaining: 14

Prerequisites (Courses and Tests)
This course requires the following prerequisite
ICS 311 - Database Management Systems

Restrictions
  • Restricted to the following major(s): Computer Forensics, Computer Information Technology, Computer Application Development, Computer Science, Data Science, Cybersecurity

Add/Drop/Withdraw
Full refund is available until August 30, 2019, 11:59PM CST.
Adding course is closed. Dropping course is closed.
The last day to withdraw from this course is November 25, 2019.

Tuition and Fees (Approximate)

Tuition and Fees (approximate):

Tuition -resident: $937.44
Tuition -nonresident: $1,912.64
Approximate Course Fees: $144.44

Course Level
Undergraduate

Description
The field of computer science is experiencing a transition from processing-intensive to data-intensive problems, wherein data is produced in massive amounts by large sensor networks, simulations, and social networks. Efficiently extracting, interpreting, and learning from these very large data sets need different storage and processing requirements compared to traditional business applications that are mostly dependent on relational database management systems. These emerging data-intensive applications require heavy read/write workloads and do not need some of the stringent schema and ACID properties that are central to relational databases. To cope with these requirements, a new genre of large-scale systems, is introduced that is called NoSQL databases. The main characteristics of NoSQL databases are that they are open source, non-schema oriented, having weak consistency properties and heavily distributed over large and clusters of commodity hardware. In this course, we will cover the basic concepts and approaches that are used by such big-data systems. Students will gain hands-on experience by solving relevant problems through projects utilizing publicly available systems. Topics covered includes: fundamentals of big data storage and processing using Hadoop, distributed file systems, and map-reduce, fundamentals of the four categories of NoSQL systems, namely kay-value stores, document stores, column stores, and graph stores. Students will implement applications using the following systems: Apache HBase, Amazon's Dynamo, Apache Cassandra, MongoDB, and Neo4J.

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