1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2019/2020
4. Biznes elektroniczny - Program Erasmus pierwszego stopnia
5. Big data technologies

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Big data technologies - opis przedmiotu

Cel przedmiotu

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.

Wymagania wstępne

Zapisz zmiany

Introduction to databases, Basics of statistics

 

Zakres tematyczny

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.

Metody kształcenia

Lecture, individual projects.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

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%

 

Literatura podstawowa

1. MongoDB, Cassandra, Redis, Neo4J webpages
2. Elasticsearch webpage

Literatura uzupełniająca

Uwagi

Zmodyfikowane przez dr hab. inż. Artur Gramacki, prof. UZ (ostatnia modyfikacja: 09-12-2019 00:21)