In the world of Database Management Systems (DBMS) we come around with multiple confusing terms related to it. In this blog post, we will cover some key terms related to Database Management Systems and explain them in simple language.
Note: If you are a beginner and don’t understand these terms, we suggest you to read twice or thrice the same term.
A database is a well-structured collection of data which is organized in a way that allows for storage, retrieval, and manipulation of data.
Example: A database for an e-commerce website like Amazon that stores information about products, customers, and orders.
DBMS (Database Management System)
The DBMS (Database Management System) is the software or system that enables users to create, manage, and interact with databases. Examples of DBMS are – MySQL, Oracle, SQL Server, and PostgreSQL.
Example: If you’re using PostgreSQL to store customer data for your online store, PostgreSQL is the Database Management System (DBMS).
RDBMS (Relational Database Management System)
An RDBMS (Relational Database Management System) is a type of Database Management System (DBMS) that stores data in the form of tables, with relationships between them. RDBMS systems, such as MySQL, Oracle, and PostgreSQL, use Structured Query Language (SQL) to manage and query data.
Example: In a university database, an RDBMS can store information about students, courses, and professors in structured tables with relationships between them.
SQL (Structured Query Language)
SQL (Structured Query Language) is a standardized programming language used to communicate with Database Management System (DBMS). It allows you to create, modify, and query databases.
Example: To retrieve a list of all products priced below 20 dollar from the e-commerce database, you might use an SQL query like
SELECT * FROM products WHERE price < 20;.
A table is the fundamental structure in a relational database, consisting of rows and columns.
Example: In a customer table, each row (tuple) could represent a unique customer, with attributes like name, email, and address.
A row, also known as a tuple or record, represents a single data entry in a table. It contains values related to each column in the table.
Example: In the products table of our e-commerce database, each row could represent a specific product with attributes such as name, price, and description.
A primary key is a unique identifier for each row (tuple) in a table, which ensures the data integrity and enabling data retrieval efficiently. It helps to identify the data uniquely.
Example: In the customer table, a unique customer ID can be the primary key.
A foreign key is a field in one table that establishes a link or relationship with the primary key in another table. It helps maintain data integrity and relationship between multiple tables.
Example: In an order table, a foreign key could reference the customer ID in the customer table to associate an order with a specific customer.
An index is a data structure that improves the speed of data retrieval operations on a database table. It works like a table of contents for your database, making searches faster.
Example: Imagine an index on the product name column in the products table, allowing for faster searches for specific products by name.
Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down large tables into smaller, related tables.
Example: Instead of storing customer addresses in multiple places, normalization involves creating a separate address table and linking it to the customer table through foreign keys.
A schema is a blueprint that defines the structure and organization of a database, including tables, relationships, constraints, and security permissions.
Example: A database schema for a blog platform might include tables for users, articles, comments, and their relationships.
A query is a request for data from a database using SQL. Queries can retrieve, update, insert, or delete records in the database.
Example: You can use a query to retrieve a user’s profile information from the users table by specifying the user’s ID.
Backup and Restore
Backup and Restore is the process of creating copies of a database to prevent data loss and recovering data from those copies in case of a failure.
Example: Regularly backing up your e-commerce database ensures that you can restore it to a previous state if there’s a data loss or system failure.
A transaction is a sequence of one or more SQL operations that are treated as a single unit of work. Transactions ensure data consistency and integrity.
Example: When a customer places an order, the transaction may involve updating inventory, creating an order record, and processing payment—all as a single, atomic operation.
Concurrency Control involves techniques and mechanisms used to manage multiple users or processes accessing a database simultaneously while preventing data inconsistencies.
Example: In a multi-user database, concurrency control mechanisms ensure that two users can’t simultaneously update the same data in a way that causes conflicts.
ACID (Atomicity, Consistency, Isolation, Durability)
ACID is a set of properties that guarantee reliable processing of database transactions. It ensures that database transactions are processed reliably even in the presence of system failures.
Example: In a banking system, ACID guarantees that a fund transfer transaction is either completed successfully, leaving both accounts consistent, or not completed at all, without any loss of funds.
NoSQL databases are a category of database systems that provide flexible data models, making them suitable for unstructured or semi-structured data. Unlike traditional relational databases, NoSQL databases do not rely on fixed schemas, allowing for scalability and versatility. Common types of NoSQL databases include document stores, key-value stores, column-oriented databases, and graph databases.
Example: MongoDB is a popular document-oriented NoSQL database used for applications where data structures can evolve over time.
Metadata is data about data. It provides information about the structure, content, and properties of the database. In DBMS, metadata includes details about tables, columns, data types, constraints, indexes, and other database objects.
Example: Metadata can include information about the data type of a column, such as “string” or “integer,” and constraints like “unique” or “not null.”
Data redundancy occurs when the same data is stored in multiple places within a database. While some level of redundancy can be intentional and beneficial for performance optimization or denormalization, excessive data redundancy can lead to data inconsistency and inefficiency.
Example: Storing a customer’s address in multiple tables can lead to data redundancy if the address changes and needs to be updated in multiple places.
Key-value is a data storage and retrieval model used in NoSQL and some other database systems. In this model, data is stored as a collection of key-value pairs, where each piece of data (the “value”) is associated with a unique identifier (the “key”). Key-value databases are known for their simplicity and high performance in scenarios where rapid data retrieval and scalability are crucial.
Example: Redis is a popular key-value store used for caching frequently accessed data.
A document store is a type of NoSQL database that stores and retrieves data in a format similar to semi-structured documents, typically in JSON or BSON (binary JSON) format. In a document store database, data is organized into collections or buckets, and each document within a collection can have a unique structure.
Example: MongoDB is a well-known document store database that can store data in JSON-like documents.
A column-oriented database is a type of database management system optimized for storing and retrieving data by columns rather than rows. This architecture allows for efficient data compression, reduced I/O, and faster query performance when only specific columns are needed.
Example: Apache Cassandra is a column-oriented database used for handling large amounts of time-series data.
We hope this blog is going to be helpful for you. Keep coming back to this blog post for revision, and feel free to ask your queries in the comment section below.