Topics 10
Introduction to Database Management Systems
Overview of database management systems (DBMS), their importance in modern information sys...
Relational Database Model
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SQL Fundamentals
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Database Design and Modeling
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Database Security and Integrity
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Indexing and Query Optimization
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Transaction Management and Concurrency Control
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Database Connectivity and Application Development
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NoSQL Databases
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Big Data and Distributed Databases
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Unit Outline 40h
Learning Objectives
5 objectives- Understand the fundamental concepts and components of database management systems (DBMS).
- Apply relational database modeling techniques, including normalization and entity-relationship diagrams.
- Develop proficiency in SQL for data definition, manipulation, and retrieval.
- Explore advanced topics such as database security, indexing, transaction management, and concurrency control.
- Gain an overview of NoSQL databases and big data technologies for handling large-scale, unstructured data.
Content Outline
PreviewUnit 614: Database Management Systems and Big Data Technologies
1. Introduction to Database Management Systems
- Definition and purpose of DBMS
- Importance of DBMS in modern information systems
- Basic concepts: data, database, DBMS software
- Components of DBMS: DBMS engine, database schema, query processor, transaction manager
- Types of DBMS (brief overview)
2. Relational Database Model
- Fundamentals of the relational model
- Terminology: tables (relations), rows (tuples), columns (attributes)
- Keys: primary keys, foreign keys, candidate keys
- Relationships: one-to-one, one-to-many, many-to-many
- Normalization techniques:
- Purpose of normalization
- Normal forms (1NF, 2NF, 3NF, BCNF)
- Denormalization considerations
3. SQL Fundamentals
- Introduction to Structured Query Language (SQL)
- Data Definition Language (DDL): CREATE, ALTER, DROP
- Data Manipulation Language (DML): SELECT, INSERT, UPDATE, DELETE
- Data Control Language (DCL): GRANT, REVOKE (briefly)
- Query basics: filtering, sorting, joining tables
- Aggregate functions and grouping data
4. Database Design and Modeling
- Importance of database design
- Entity-Relationship Diagrams (ERD): entities, attributes, relationships
- Mapping ERD to relational schema
- Use of data modeling tools (e.g., ERwin, MySQL Workbench)
- Best practices for efficient and scalable database structures
5. Database Security and Integrity
- Database security principles
- Access control models: discretionary, mandatory, role-based
- Authentication and authorization mechanisms
- Encryption techniques for data protection
- Data integrity enforcement:
- Constraints (primary key, foreign key, unique, check)
- Triggers
- Transaction management fundamentals
6. Indexing and Query Optimization
- Purpose and types of indexing:
- B-tree, Hash indexes
- Query execution plans
- Query optimization techniques
- Performance tuning strategies
- Understanding and analyzing database profiler tools
7. Transaction Management and Concurrency Control
- Definition of a database transaction
- ACID properties: Atomicity, Consistency, Isolation, Durability
- Isolation levels and their impact
- Concurrency control mechanisms:
- Locking strategies (pessimistic and optimistic)
- Deadlock detection and resolution
- Handling data consistency in multi-user environments
8. Database Connectivity and Application Development
- Database connectivity concepts
- Connecting to databases using programming languages (Java JDBC, Python DB-API)
- Implementing CRUD operations programmatically
- Managing database transactions within applications
- Error handling and resource management
9. NoSQL Databases
- Introduction to NoSQL and its emergence
- Types of NoSQL databases:
- Document-oriented (e.g., MongoDB)
- Key-value stores (e.g., Redis)
- Column-family stores (e.g., Cassandra)
- Graph databases (e.g., Neo4j)
- Differences between NoSQL and relational databases
- Use cases and scenarios for NoSQL
10. Big Data and Distributed Databases
- Overview of big data concepts
- Distributed database systems and architectures
- Challenges in scalability and data consistency
- Data partitioning and replication strategies
- Parallel processing frameworks
- Technologies overview:
- Hadoop ecosystem
- Apache Spark
- Applications in managing and analyzing massive datasets
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