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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BIE-BIG.21 DB Technologies for Big Data Extent of teaching: 2P+2C
Instructor: Gattermayer J. Completion: KZ
Department: 18102 Credits: 5 Semester: L

Annotation:
Students will be introduced into the field of Big Data processing where nonrelational (NoSQL) database engines are typically used today. The course is focused practically so that after finishing the course students were able to choose suitable tools (mostly open source) and techniques,design and implement a simplest reproducible method of data processing (data collection, transformation/aggregation, presentation). Students get acquainted with various architectures for processing and storing big data. A theoretical foundation and presentation of individual technologies will be supplemented with specific case studies.

Lecture syllabus:
1. Introduction to the Big Data processing, the definition of the Big Data concept, CAP theorem.
2. Case study.
3. [2] Column-oriented database engines (Cassandra).
5. Document-oriented database engines (MongoDB).
6. [2] Platforms for Big Data processing based on maintaining data in a file system (Hadoop).
8. [2] Platforms for Big Data processing based on maintaining data in main memory (Spark).
10. Indexing of unstructured and semistructured data (ElasticSearch, Solr).
11. Tools for data visualization and presentation (Kibana).
12. [2] Case studies.

Seminar syllabus:
1. Introduction to the laboratory environment
2. Introduction to working with Cassandra Cluster
3. Hadoop MapReduce
4. Cassandra UseCase 1 - Part 1
5. Cassandra UseCase 1 - Part 2
6. Cassandra UseCase 2 - Part 1 (Hive / Pig Use)
7. Cassandra UseCase 2 - Part 1
8. Cassandra UseCase 3 - Part 1 (Use Solr)
9. Cassandra UseCase 3 - Part 2
10. Cassandra UseCase 4 - Part 1 (Complex solution)
11. Cassandra UseCase 4 - Part 2
12. Submission of semester work, credit
13. Reserve

Literature:
1. Zikopoulos P., Eaton Ch. : Understanding big data: Analytics for enterprise class Hadoop and streaming data. McGraw-Hill Osborne Media, 2011. ISBN 978-0071790536.
2. Hewitt E. : Cassandra: The Definitive Guide. O'Reilly Media, 2010. ISBN 978-1449390419.
3. Meier A., Kaufmann M. : SQL & NoSQL Databases. Springer, 2019. ISBN 978-3-658-24549-8.
4. Bradshaw S., Brazil E., Chodorow Ch. : MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O'Reilly Media, 2019. ISBN 9781491954461.

Requirements:
Basic knowledge of relational databases, working with the command line.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/BI-BIG/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BIE-PV.21 Computer Systems and Virtualization 2021 PV 6


Page updated 28. 3. 2024, semester: Z/2023-4, L/2019-20, L/2022-3, Z/2019-20, Z/2022-3, L/2020-1, L/2023-4, Z/2020-1, Z,L/2021-2, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška