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.
- Teacher: Noboa Erick
- Teacher: Rita Fleiner
Credit value: 4
Field of knowledge: Information technology and technology
Course host institution: Óbudai Egyetem
Course language: English
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.
Curriculum:
T: Introduction. Knowledge assessment. Relational database systems. L:Basic SQL exercises
T: Data modelling, single-relationship data model. L: Multi-table queries.
T: Normal forms, dependencies, decomposition of relations. L: DDL, constraints.
T: Relational algebra, relational data model. L: DML, views.
T: Data storage, file organisation, indexes. L: Grouping functions (GROUP BY, HAVING statement parts)
T: Query processing, query optimization. L: Transaction handling
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics,
selectivity, cost, materialization, pipelining. L: Execution plan analysis.
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics, selectivity, cost, materialization, pipelining. L: Execution plan analysis.
Midterm test 1
T: NoSQL databases. Cassandra: concepts, architecture, queries. L: Cassandra in practice.
T: NoSQL databases. MongoDB: concepts, architecture, queries. L: MongoDB in practice
T: Basics of Big data. Hadoop framework. L: Spark in practise
T: Basics of Big data. Apache Spark. L: Spark in practise.
Midterm test 2
T: Data modelling, single-relationship data model. L: Multi-table queries.
T: Normal forms, dependencies, decomposition of relations. L: DDL, constraints.
T: Relational algebra, relational data model. L: DML, views.
T: Data storage, file organisation, indexes. L: Grouping functions (GROUP BY, HAVING statement parts)
T: Query processing, query optimization. L: Transaction handling
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics,
selectivity, cost, materialization, pipelining. L: Execution plan analysis.
T: Database tuning. Execution plan, access paths, indexes, join types, CBO statistics, selectivity, cost, materialization, pipelining. L: Execution plan analysis.
Midterm test 1
T: NoSQL databases. Cassandra: concepts, architecture, queries. L: Cassandra in practice.
T: NoSQL databases. MongoDB: concepts, architecture, queries. L: MongoDB in practice
T: Basics of Big data. Hadoop framework. L: Spark in practise
T: Basics of Big data. Apache Spark. L: Spark in practise.
Midterm test 2
EKKR/MKKR: Beállítás folyamatban