Databases and Distributed Systems

Section 1: Introduction to Databases

A database is a structured set of data. It’s used to store, manage and retrieve information.
Databases come in various forms, but at a basic level, they may comprise of tables of data, where each table has a number of different fields.
Database Management Systems (DBMS) are software applications used to interact with databases and manipulate the data inside them.

Section 2: Relational Database

The most common type of database is a relational database, which organises data into one or more tables.
These tables are connected by relationships, which is how data in one table can relate to data in another.
A common relational database system is SQL (Structured Query Language). SQL is used to communicate with a database and is particularly effective for handling structured data.

Section 3: Distributed Systems

A distributed system is a model in which components located on networked computers communicate and coordinate their actions by passing messages.
The components interact with each other in order to achieve a common goal.
An example of a distributed system is the internet, which is a network of networks sharing resources and communicating via protocols.

Section 4: Advantages and Disadvantages of Distributed Systems

Advantages of distributed systems include: scalability (systems can easily be expanded by adding more machines), reliability (if one machine fails, the system as a whole can still operate), and resource sharing (computers can access resources that are not available on their local system).
Disadvantages include: complexity (ensuring correct communication and coordination between distributed components can be complex), security issues (open network communication can put data at risk), and performance issues (network latency can impact performance).

Section 5: Data Warehousing and Big Data

Data warehousing is an integration of diverse data from a variety of sources to make it accessible for querying and analysis.
Big data is a field which deals with data sets that are too large or complex for traditional data-processing application software to adequately deal with.
Techniques like data mining are used to discover patterns in large data sets. This has become increasingly important with the boom of big data.