Database System Terminologies

The foundation of database systems is a broad vocabulary that facilitates communication about the numerous facets of data storage, administration, and retrieval. In order to further your comprehension, we will go through the important terms used in database systems in this article, along with comprehensive explanations and applicable examples.

  • Entity − An entity is a specific real-world thing or idea that we wish to represent and keep data about. For instance, students, professors, courses, and departments might all be considered entities in a university database

  • Attribute −  An attribute is a representation of a particular quality or trait of an entity. It outlines the information about the entity that we wish to store. A student entity, for instance, may include characteristics like a student ID, name, date of birth, and major.

  • Key − A key is an entity's or an entity instance's particular set of properties that uniquely identify it. For data integrity and effective data retrieval, keys are necessary. To ensure that each student has a distinct identification, the student ID, for instance, may act as the primary key in the student object

  • Table − A relational database system's core structure for organizing data into rows and columns is a table. Each table is made up of columns (attributes) and rows (records), and it represents a single entity. For instance, a table called "Students" may have columns for student data such as student ID, name, and major.

  • Primary Key − A primary key is a way for a table to be uniquely identified. It guarantees that each row in the table can be identified individually. A single column or a group of columns might serve as the primary key. The student ID column, for instance, may serve as a primary key in the "Students" database.

  • Foreign Key − A column or group of columns in one database that relate to the primary key in another table is known as a foreign key. This creates a connection between the two tables. For instance, to link students with the courses they are registered for, a foreign key in the "Courses" database can make reference to the primary key in the "Students" field.

  • Relational Database − A relational database is a kind of database system that arranges information into tables and uses keys to create relationships between those tables. It provides a systematic and effective method of managing data by adhering to the fundamentals of the relational model. Popular relational database systems include PostgreSQL, Oracle, and MySQL.

  • Query − Requesting data or information from a database is known as a query. It enables users to obtain, manipulate, and manage data and is described using a query language like SQL. For instance, a query may return a list of every student registered for a certain course.

  • Index − A database table's index is a type of data structure that accelerates data retrieval processes. Based on the indexed column(s), it enables easy access to specified data. In the "Students" database, for instance, an index on the student ID column would speed up searches looking for students by their ID.

  • Normalization − Normalization is the process of arranging data in a database to reduce duplication and strengthen data integrity. It entails breaking down tables and creating connections between them. Normal forms are a set of guidelines that the normalization process abides by. A normalization process can involve dividing a single database containing student and course data into distinct "Students" and "Courses" tables.

  • ACID − Atomicity, Consistency, Isolation, and Durability, or simply ACID, are characteristics that guarantee the dependability and integrity of database transactions. A transaction will always be seen as a single piece of work thanks to atomicity. A transaction moves the database from one legitimate state to another by guaranteeing consistency. Concurrent transactions are prevented from interfering with one another through isolation. Durability ensures that changes made during a transaction are permanent and will endure any future system failures.

  • Data Warehouse − An organization's data warehouse is a sizable, integrated, and unified collection of information from numerous sources. It is intended to be used for decision-making, reporting, and analysis. For the purposes of business intelligence and data analytics, data warehouses often store historical and aggregated data

  • Data Mining − Finding patterns, trends, and insights from huge databases is referred to as data mining. In order to extract useful knowledge and information, statistical and machine-learning approaches are applied. To find hidden patterns and provide predictions or suggestions based on the data, data mining techniques are utilized.

  • Backup and Recovery − Backup and recovery procedures are crucial for guaranteeing data availability and guarding against data loss. In order to offer a restoration point in the event of a system failure or data corruption, backup entails making copies of the database at regular intervals. Recovery entails utilizing the backup copies to restore the database to a consistent condition.

  • To guarantee that client data can be recovered in the event of hardware failures or unintentional deletions, a database administrator, for instance, may plan daily backups of a customer database.

  • Replication − Replication is the process of making and keeping copies of a database or specific sections of a database on several servers. It increases fault tolerance, scalability, and data availability. Asynchronous or synchronous replication guarantees that changes made to one duplicate are replicated to the others.

  • For instance, in a distributed e-commerce system, product information may be duplicated over several servers to make sure that users can easily access product details no matter where they are.

  • Data Dictionary − A data dictionary, sometimes referred to as a metadata repository, is a central repository for details on the objects and the schema of a database. It includes metadata including table and column names, data types, restrictions, and table connections. The DBMS uses the data dictionary to verify queries, uphold data integrity, and offer details on the database architecture.

  • The "Employees" table, for instance, may be described in the data dictionary together with the names and data types of its columns, such as "Employee ID," "First Name," "Last Name," and "Salary."

  • Database Schema − A database schema outlines the logical organization and structure of a database. The tables, columns, data types, restrictions, and connections between the tables are all described. A blueprint for building and running the database is provided by the schema. A database design for an online shop, for instance, would have tables like "Books," "Authors," and "Orders," each with specific fields, data types, and connections.


It's essential to comprehend the terminologies used in database systems in order to effectively communicate ideas and procedures related to data storage, administration, and retrieval. The terms entity, attribute, table, primary key, foreign key, relational database, query, index, normalization, ACID properties, data warehousing, data mining, backup and recovery, data migration, and replication were all covered in detail in this article. Knowing these terms will help you better grasp database principles and designs while allowing you to move confidently across the world of database systems.

Updated on: 02-Aug-2023


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