A graph database is a database that uses a graphical model to represent and store the data.
The graph database model is an alternative to the relational model.
In a relational database, data is stored in tables using a rigid structure with a predefined schema.
In a graph database, there is no predefined schema as such. Rather, any schema is simply a reflection of the data that has been entered. As more varied data is entered, the schema grows accordingly.
This article is part 2 of the database tutorial.
Here, we cover:
This article is part 1 of the database tutorial.
Here, we cover the following:
This database tutorial is for beginners. It explains basic concepts and assumes no prior knowledge of databases.
You don’t need to follow along – there aren’t any exercises. But there are plenty of screenshots and a few diagrams. The purpose of the tutorial is to introduce you to the basic concepts of databases.
An MPP database is a massively parallel processing database (MPP stands for Massively Parallel Processing).
Massively parallel processing refers to the use of a large number of processors (or separate computers) to perform a set of coordinated computations in parallel (simultaneously).
ORDBMS stands for Object-Relational Database Management System.
An ORDBMS is a database management system that is a hybrid between the object-oriented model (OODBMS) and the relational model (RDBMS).
Each of those two models has their strengths and weaknesses. By combining the two models, a DBMS can take advantage of various strengths from each model.
OODBMS stands for Object-Oriented Database Management System.
An object-oriented database management system (also known simply as an object database) is a DBMS where data is represented in the form of objects, as used in object-oriented programming.
In contrast to relational database management systems (RDBMSs), where data is stored in tables with rows and columns, an object-oriented database stores complex data and relationships between data directly, without mapping to relational rows and columns.
A data warehouse is a large collection of data that can be used to help an organisation make key business decisions.
Here’s a more precise definition of the term, as coined by Bill Inmon, (considered by many to be “the father of data warehousing”):
A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions.
The term big data refers to the massive amounts of data – both structured and unstructured – that inundate organisations on a day-to-day basis.
Typically, big data is so large, and accumulates so fast, that traditional data storage and processing applications are inadequate.
The big data industry helps organisations capture and analyse their big data, so that those organisations can make more informed business decisions.
Below is an alphabetical list of 121 relational database management systems (RDBMSs). Some of these could be classified under other categories, such as NoSQL databases, or object-relational. However, they are all relational to some degree.
