1. komponenta

Lecture type	Total
Lectures	30
Exercises	15

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: Getting familiar with some advanced topics in the area of database systems, data warehouses, NoSQL paradigm and Big Data processing.

COURSE DESCRIPTION AND SYLLABUS:
1. Expanding knowledge of relational databases. Complex data types. Distributed databases. Aims, purposes, advantages and flaws of data distribution. Structure of the distributed database, replication, fragmentation. Distributed transactions protocols.
2. NoSQL paradigm. Why NoSQL? Aggregated data model. NoSQL database properties. Consistency and CAP theorem. Types of NoSQL database: key-value, document, graph and column-family databases. Map/reduce algorithm in the NoSQL world.
3. Data warehouses. Aims and purposes. Data warehouse models. The process of data warehouse creation. OLAP. ETL (extract-transform-load) process.
4. Big Data. Distributed file systems. Google FS and HDFS. Apache Hadoop ecosystem. Map/reduce algorithm. Distributed calculations and analyses. Apache Spark.

1. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Pramod J. Sadalage, Martin Fowler, Addison Wesley, 2012.
2. The Data Warehouse Toolkit - The Complete Guide to Dimensional Modeling, 2nd edition, R. Kimball, M. Ross, John Wiley & Sons, 2002.
3. Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement, Eric Redmond, Jim Wilson, Pragmatic Bookshelf, 2012.
4. Hadoop in practice, Alex Holmes, Manning, 2012.
5. Database System Concepts, 4th edition, A. Silberschatz, H. F. Korth, S. Sudarshan, McGraw-Hill, 2001.

Izborni računarski predmet 1, 2 - Regular study - Mathematics and Computer Science Education

Izborni računarski predmet 1, 2 - Regular study - Mathematics and Computer Science Education