Emerging Database Technologies and Architectures
The chapter explores advanced database technologies and architectures, highlighting trends that address challenges posed by modern data management. It covers distributed databases, data warehousing, data mining, NoSQL databases, cloud databases, and Big Data concepts, offering a comprehensive view of how these systems evolve to support diverse data needs. Finally, it discusses future trends such as serverless and autonomous databases, emphasizing the necessity of adapting to a rapidly changing data landscape.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Distributed databases enhance availability and scalability but come with increased complexity.
- Data warehousing allows for improved analysis and reporting through ETL processes and OLAP functionalities.
- NoSQL databases cater to new data types and scaling needs, often sacrificing some consistency for availability.
Key Concepts
- -- Distributed Database
- A database that stores data across multiple interconnected computers, appearing as a unified system to the user.
- -- Data Warehousing
- A repository storing integrated and analyzed data to support decision-making, characterized by its subject-oriented and non-volatile nature.
- -- OLAP vs. OLTP
- OLAP supports complex analytical queries on historical data, while OLTP handles daily transaction processing with a focus on current operational data.
- -- NoSQL Databases
- A collection of database technologies designed to manage unstructured or semi-structured data, optimizing for scalability and availability.
- -- DBaaS
- Database as a Service, a cloud computing model allowing users to access database capabilities without managing physical infrastructure.
- -- Big Data
- Data sets characterized by high volume, velocity, and variety that require specialized processing and storage solutions.
Additional Learning Materials
Supplementary resources to enhance your learning experience.