Table of Contents - Raspodijeljene i nerelacijske baze podataka

Completed Lectures

Lecture 00: Odabrana poglavlja iz relacijskih baza podataka

• Introduction to relational databases
• Basic concepts: tables, keys, relationships
• SQL basics: SELECT, FROM, WHERE
• Joins and normalization
• Indexes
• Practical applications

Lecture 01: Uvod u raspodijeljene i nerelacijske baze podataka

• NoSQL revolution and database evolution
• Explosion of data: volume, variety, velocity
• ACID transactions
• NoSQL database types: key-value, document, column-family, graph, vector
• Advantages and disadvantages of NoSQL
• Vector databases and AI/LLM integration

Lecture 02: Moderni pristup pohrani podataka

• Big Data challenges (3V-5V)
• NoSQL vs relational databases
• CAP theorem
• BASE properties
• Types of NoSQL databases with examples
• Use cases for different NoSQL databases

Lecture 03:

• Part A: Project selection and developer tools

  • LLM tools for projects
  • AI-powered coding tools
  • No-Code/Low-Code tools
  • Free database services
  • Git workflow for projects

• Part B: Denormalization and Transactions

  • Normalization fundamentals
  • Normal forms (1NF to 3NF)
  • Denormalization strategies
  • Transaction concepts and ACID properties
  • Distributed transactions
  • Eventual consistency
  • NoSQL transactions and patterns

Lecture 04: SQL Programiranje

• Stored procedures
• Functions
• Triggers
• Control structures in SQL
• Application architecture integration
• Views and materialized views
• Best practices and performance considerations
• DuckDB: Modern analytical SQL database
  • Embedded database architecture
  • Column-oriented storage for analytics
  • Integration with data science tools
  • Comparison with traditional databases

Draft Lectures

Lecture 05: MongoDB and Document-Oriented Databases

• JSON to MongoDB evolution
• MongoDB vs relational databases
• CRUD operations
• Query operators
• Aggregation pipeline
• Indexing
• Schema design best practices
• MongoDB Atlas
• Security and monitoring

Lecture 06: Cloud and Distributed Databases

• Parallel vs distributed databases
• Distributed database architectures
• Partitioning and sharding
• MS SQL Server distribution techniques
• Cloud database services
• Comparison of cloud and traditional databases
• Case studies: banking system and e-commerce
• Performance optimization

Lecture 07: NoSQL Database Fundamentals

• Reasons for NoSQL databases
  • Scalability (Scale Up vs Scale Out)
  • Cost considerations
  • Flexibility
  • Availability
• Distributed database management
• CAP theorem practical examples
• ACID vs BASE consistency models
• Types of NoSQL databases revisited
• Eventually consistent systems

Lecture 08: Redis and Key-Value Stores

• Redis fundamentals
• Redis vs MongoDB comparison
• Redis data types: strings, lists, sets, sorted sets, hashes, streams
• Redis as cache
• Redis transactions
• Persistence options
• Redis cluster and scaling
• Security best practices
• Common use cases

Lecture 09: Column-Family Databases

• Row-oriented vs column-oriented storage
• Column-family database architecture
• Structure: keyspace, row keys, columns, column families
• Bigtable: Google's influence on column stores
• Internal structure and configuration parameters
• Design principles for column databases
• Comparison with relational databases
• Use cases for column stores

Lecture 10: Graph Databases

• Graph theory basics: vertices, edges, paths
• Types of graphs: directed, undirected, weighted
• Graph properties: isomorphism, order, size, degree
• Graph database terminology and models
• Cypher query language
• Gremlin traversal language
• Graph database design principles
• Social network case study
• Practical applications

Lecture 11: Document Databases

• Document database concepts
• Comparison with relational databases
• Collections and documents
• Schema design: schemaless vs polymorphic schemas
• CRUD operations
• Embedded documents vs references
• Normalization vs denormalization in document databases
• Indexing strategies
• Case study: transportation and shipping system
• Partitioning and sharding approaches

Lecture 12: Vector Databases

• Introduction to vector databases
• Vector embeddings and their creation
• Architecture and principles of vector databases
• Algorithms for vector similarity search (HNSW, IVF)
• Comparison of leading vector databases
• Applications in AI and LLM systems
• Retrieval Augmented Generation (RAG)
• Performance optimization techniques
• Practical examples and case studies
• Future trends and challenges