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Lecturer(s)
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Borkovcová Monika, Ing. Ph.D.
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Course content
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1. Introduction to the subject, distributed data solutions, basic concepts 2. Introduction to NoSQL databases and the BigData ecosystem 3. Key-value NoSQL databases 4. Document-oriented NoSQL databases 5. Column-oriented NoSQL databases 6. Graph-oriented NoSQL databases 7.-9. ELK - Elasticsearch, Logstash, Kibana 10. Hadoop & Apache Spark ecosystem 11. Data Science and the use of Python for data analytics and its possibilities
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Demonstration, Skills training
- Home preparation for classes
- 66 hours per semester
- Preparation for an exam
- 36 hours per semester
- Contact teaching
- 12 hours per semester
- Term paper
- 66 hours per semester
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Learning outcomes
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The aim of the course is to familiarize students with unstructured databases, including the NoSQL database paradigm. It also aims to introduce and demonstrate the practical use of Big Data, including individual tools. After completing the course, students will understand how NoSQL databases work and will understand the principles and possibilities of Big Data.
Students will learn distributed data solutions, configure and implement NoSQL databases. They will understand the functioning of selected platforms and tools for big data management and data analysis.
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Prerequisites
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To complete this course, students are expected to have advanced knowledge of relational database systems, knowledge of the principles of algorithmization and programming and operating systems, and the ability to analyze and solve given problems.
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Assessment methods and criteria
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Written examination, Home assignment evaluation, Self project defence
ASSESSMENT In order to receive assessment, it is necessary to defend a complex project submitted by the deadline focused on the selected NoSQL database/data storage/tool. EXAM Verification of the study results is in the form of a written examination test. The topics covered in the exam verify the acquired knowledge in the field of NoSQL databases, BigData, data stores and data science, both from lectures and exercises. The test includes both theoretical and practical exercises. Successful completion of the project and obtaining credit is considered to be the threshold of obtaining 70% of the possible points from the credit project, i.e., it is necessary to obtain at least 35 points from a total of 50 possible points; the project also constitutes 50% of the overall assessment. To pass the exam, students must obtain 70% of the points from the exam test, i.e., at least 35 points out of a total of 50 possible points. The total of all points from the credit and exam must be at least 70%, i.e., 70 points, while maintaining the minimum limits of 35 points from the credit and 35 points from the exam. 100 - 94 % - A 93 - 88 % - B 87 - 82 % - C 81 - 76 % - D 75 - 70 % - E less - F
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Recommended literature
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