Academic year/semester: 2026/27/1

ECTS Credits: 3

Available for: Only for the faculty’s students

Lecture hours: 2
Seminarium:-
Practice: -
Laboratory: -
Consultation: -

Prerequisites: -

Course Leader: Dr. FLEINER Rita 

Faculty: John von Neumann Faculty of Informatics, 1034 Budapest, Bécsi út 96/b

Course Description:
Relational data model, relational algebra, RDBMS architecture, logical and physical data model, database design, normal forms. Database management in Oracle environment database instances, memory structures, transactions. Execution planning, optimization, SQL tuning. Index structures, join methods. NoSQL database types and their operation, their relation to Big Data systems. Understanding the use of MongoDB and Cassandra database management systems: basics, architecture, queries. Big data basics and the Hadoop framework. Apache Spark. 

Competences:
In the course, students learn the principles and implementation of relational database management, the process of database design and modern data management methods. During the course, students will gain insights into the world of non-relational database management and Big Data, and will become familiar with the concepts, procedures and tools of NoSQL and Big Data data storage. 

Topics:
Lecture schedule 
Education week 
Topic 
1. 
T: Introduction. Knowledge assessment. Relational database systems. L:Basic SQL exercises. 
2. 
T: Data modelling, single-relationship data model. L: Multi-table queries. 
3. 
T: Normal forms, dependencies, decomposition of relations. L: DDL, constraints. 
4. 
T: Relational algebra, relational data model. L: DML, views. 
5. 
T: Data storage, file organisation, indexes. L: Grouping functions (GROUP BY, HAVING statement parts). 
6. 
T: Query processing, query optimization. L: Transaction handling. 
7. 
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics, selectivity, cost, materialization, pipelining. L: Execution plan analysis. 
8. 
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics, selectivity, cost, materialization, pipelining. L: Execution plan analysis. 
9. 
T: NoSQL databases. Cassandra: concepts, architecture, queries. L: Cassandra in practice. 
10. 
T: NoSQL databases. MongoDB: concepts, architecture, queries. L: MongoDB in practice.  
11. 
T: Basics of Big data. Hadoop framework. L: Spark in practise. 
12. 
T: Basics of Big data. Apache Spark. L: Spark in practise. 
13. 
T: Test (theory + practise) 
14. 
T: Test replacement 

Assessment: Mid-term requirements  Conditions for obtaining a mid-term grade/signature  Students have to pass at least 51% in both tests (theory and practise).  Assessment schedule  Education week  Topic  13  Theory test, Lab test  14  Theory test replacement, Lab test replacement  The mid-term grade is determined by the sum of the points obtained in the tests.  Type of the replacement of written test/mid-term grade/signature  Both tests can be replaced in the 14th week and at the beginning of the exam period. 

Exam Types:

Mid Term Exam

Compulsory bibliography: Jeffrey D. Ullman; Jennifer Widom: Adatbázisrendszerek – Alapvetés (2. kiadás), Panem, 2009. Budapest, ISBN: 9635454815    Elmasri, R., Navathe, S. B.:Fundamentals of Database Systems 7th Edition, ISBN: 978-0133970777  

Recommended bibliography: Alex Holmes: Hadoop In Practice, 2nd Edition, September 2014, ISBN 978-1-617-29222-4    Dirk deRoos, Paul C. Zikopoulos, Roman B. Melnyk PhD, Bruce Brown, Rafael Coss: Hadoop for Dummies, 2014 John Wiley & Sons, Inc., Hoboken, New Jersey, ISBN 978-1-118-65220-6 

Additional bibliography: -

Additional Information: -