SylabUZ
| Course name | Big data technologies |
| Course ID | 11.3-WE-BizElP-TechBigData-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | E-business |
| Education profile | practical |
| Level of studies | First-cycle Erasmus programme |
| Beginning semester | winter term 2019/2020 |
| Semester | 3 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Credit with grade |
| Project | 30 | 2 | - | - | Credit with grade |
Teaching students how to choose the right data analysis techniques depending on the scale of the problem being considered and the type of analysis being carried out.
Teaching students to work using modern platforms for data storage and processing.
Teaching students selected techniques to analyze large data sets, mainly textual.
Introduction to databases, Basics of statistics
Big Data: An introduction to the processing of large amounts of data.
Nonrelational databases: A reminder of the basic issues related to relational databases. Advantages and disadvantages of these databases. Basic problems related to the use of relational databases for the storage and processing of increasingly large amounts of data increasingly dispersed. Horizontal and vertical scaling of databases. A new concept of databases not based on the traditional relational model. CAP and BASE theory. Aggregate data models. Key-value, column, document and graph databases. Database replication. Sharing resources in databases. Map-Reduce methodology. Presentation of several selected nonrelational database systems (e.g. MongoDB, Cassandra, Redis, Neo4J, Oracle NoSQL Database).
Selected IT systems: Large-scale business analytics: modern solutions used for sending, storing and processing large data sets. Architecture of modern Big Data storage and processing systems on the example of the Elasticsearch platform. Real-time text data analytics using the ElasticSearch platform.
Lecture, individual projects.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the passing condition is to obtain a positive mark from the final test
Project– the passing condition is to obtain a positive mark from the project form
Calculation of the final grade: lecture 50% + project 50%
Modified by dr hab. inż. Artur Gramacki, prof. UZ (last modification: 09-12-2019 00:21)
