Subject

Big Data Modelling and Management

Course Title

Big Data Modelling and Management
Big data modeling and management

Code

m23_s_055

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

10 / Summer

Number of ECTS credits

Teacher

Eftim Zdravevski, Goran Velinov

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The development trends of traditional relational (SQL) database management systems, data warehouses, as well as the concepts of NoSQL and NewSQL big data management systems will be studied. The concepts of storing data on various memory media will be examined. Approaches for centralized or distributed storage, as well as logical organization by rows, columns, graphs, or documents, will be studied. Methods for partitioning and indexing structured, (semi/un)structured, and textual data will be studied.
Real-world approaches and solutions will be covered for overcoming the challenges of modeling, management, implementation, and deployment of big data systems. By the end of the course, students will know which systems are most suitable and what steps are required to introduce big data systems in companies, as well as the challenges companies face.

11.

Course content

A new perspective on data warehouses: conceptual, logical, and physical models; data lake concepts. Overview of big data management systems. Data modeling in big data systems: implications of time in data modeling.
Concepts of database organizations by columns (MonetDB, HBase, Cassandra), by key-value (DynamoDB, Riak), by documents (MongoDB, CouchDB), and in graphs (Neo4j, OrientDB).
Transactional and analytical databases running in main memory. Alternative data storage media.
Indexing and partitioning strategies and their impact on scalability and performance; Text indexing databases (Solr, Elasticsearch)
Integration of various data sources; Planning for development, capacity, and infrastructure.
Systems and tools for analyzing large static data, such as Spark, Spark SQL, Hive, Pig, Tez, and newer ones.
Techniques for handling data streams; systems and tools for analyzing dynamic big data such as Spark Streaming, Storm, Oozie, Sqoop, Flink, and newer.
Managing System Deployments: Expectations, Assumptions, Risks, and Team Building
Strategies and scenarios for migrations, security, and backup of big data.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 30 + 30 + 45 + 45 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	45 hours

17.

Assessment method

17.1.	Tests	30 points
17.2.	Seminar paper / project (presentation: written and oral)	45 points
17.3.	Activities and learning	20 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

completed activities 15.1 and 15.2

20.

Language of instruction

Macedonian and

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.

Required literature

22.2.

Additional literature

No. Author Title Publisher Year