Hi Learners! Today, we are going to illustrate about remarkable features of DBMS as well as characteristics of DBMS (Database Management System) with ease. Through this article, you will get to know about Top DBMS Features & Characteristics without any getting hassle.
What is the Purpose of DBMS?
Database Management System (DBMS) serves the primary objective of facilitating data retrieval and offering the dependable storage platform for getting the essential data. It most efficiently keep managing and stores business-associated data like as inventory records, customer information, financial data, and sales transactions, optimizing operations for enhanced the customer service and efficient decision-making.
DBMS makes ensuring the smooth transactions and personalized experiences, improving the overall performance in e-commerce, healthcare, and retail. It also serves the controlled and organized access to data; driving innovation and getting to help companies maintain a competitive edge.
What is the Classification of Database?
Database management system is classified at the based on several parameters like as the data model, database distribution, and user numbers. Here are the main categories of database classification; below shown each one:
Based on Data Model
Relational Database: This model, based on the relational data model, is the most popular and widely used. It organizes data into tables with rows and columns and uses SQL for data manipulation. Examples include Oracle, MySQL, and Microsoft SQL Server.
Object-Oriented Database: This model represents and stores data as objects, similar to those used in object-oriented programming. It combines database capabilities with object-oriented programming language capabilities.
Hierarchical Database: This type organizes data in a parent-children relationship, forming a tree-like structure.
Network Database: This model follows the network data model, representing data as a collection of records and relationships.
Based on User Numbers
Single-User Database System: Supports one user at a time.
Multiuser Database System: Supports multiple users concurrently.
Based on Database Distribution
Centralized Database: It helps to stores data at a centralized database system, allowing users to access data from different locations through various applications.
Distributed Database: Distributes data among different database systems of an organization, connected via communication links.
Acid Properties in Database
ACID properties are a set of characteristics that guarantee the reliability of transactions in a Database Management System (DBMS):
Also Read: What is NoSQL Database? Types and Examples
Atomicity: Making ensuring that transaction is treated as a single, indivisible unit. It either completes entirely or has no effect on the database.
Consistency: Guarantees that a transaction brings the database from one valid state to another. The database remains in a consistent state before and after the transaction.
Isolation: Ensures that the execution of one transaction is isolated from the execution of other transactions, preventing interference and maintaining data integrity.
Durability: Guarantees that once a transaction is committed, its effects are permanent and survive subsequent system failures. The changes made by committed transactions persist in the database.
What Are The DBMS Features?
Database Management System is most useful program that helps users to make interaction along with databases. Here are 25 top features of DBMS (Database Management System), including:
Data integrity refers to the overall accuracy, completeness, and consistency of data stored in a database. It is maintained by a collection of processes, rules, and standards implemented during the design phase. Data integrity is most essential that making to ensure about the information saved into database with remaining the complete, accurate, and reliable.
Data integrity is also important for regulatory compliance, including GDPR compliance and security. Data integrity is done by using the proper data validation and error checking to make ensure that sensitive data is never miscategorised or stored incorrectly; thus exposing the database to most potential risk able.
Concurrency control is essential procedure for getting to manage simultaneous operations without any conflicting with each other that making ensure the database transactions are performed concurrently and perfectly to provide the correct results without breaking any data integrity.
Also Read: 20+ Advantages and Disadvantages of SQL
Concurrency control is essential for the proper functioning of a database management system where two or more database transactions are executed simultaneously, requiring access to the same data. Some potential problems of concurrency control include:
Lost Update Problems (W-W conflict): Occur when two different transactions perform read/write operations on the same database items in an interleaved manner, resulting in incorrect values.
Inconsistent Retrievals: Occur when two different values are read for the same database item in a transaction.
To address these issues, concurrency control protocols (Timestamp ordering & Lock-based protocols) are implemented to ensure atomicity, consistency, isolation, durability, and serialize-ability of concurrent transactions.
Transaction management in a database management system (DBMS) involves ensuring the ACID properties – Atomicity, Consistency, Isolation, and Durability – of transactions to maintain data integrity and reliability.
A transaction is a logical unit of processing that represents real-world events and can consist of one or more database access operations. It is most critical for getting to manage the concurrency, and the DBMS schedules getting to access of data concurrently with allowing users to access several data without any interference.
Transactions are also used to keep managing a concurrency, satisfy ACID properties, fix read/write conflicts, use the recoverability, serialize-ability, ensure data integrity, and cascading.
DDL (Data Definition Language)
Data Definition Language (DDL) is a syntax that is going to use for creating and modifying database objects, including the tables, users, and indices. DDL is used to create and modify the structure of objects in a database using predefined commands and a specific syntax. DDL statements create, modify, and remove database objects, such as tables, indexes, and storage groups. DDL is also used in a generic sense to refer to any language that describes data. DDL includes Structured Query Language (SQL) statements to create and drop databases, aliases, locations, indexes, tables, and sequences.
(DML) Data Manipulation Language
DML is a subset of SQL that is used for getting to retrieve and manipulate data in a relational database system. DML commands include INSERT, UPDATE, DELETE, and SELECT, which are used to modify, add, and retrieve data from a database.
DML is used to manipulate data in a database, including CRUD operations (create, read, update, and delete), using commands such as INSERT, SELECT, UPDATE, and DELETE. DML commands are often part of a more extensive database language, such as SQL, and can have a specific syntax to handle data in that language.
Database security means to the many useful of tools, controls, and getting to measure developed to well-establish and preserve database confidentiality, availability, integrity, and protecting the DBMS system; and several applications are getting to access it from malicious cyber-threats and illegitimate usage.
Database security is most essential factor to avoid the data breaches, unwanted access, and data damage that can have severe consequences for individuals and businesses. Few commonly threats to database security like as insider threats, phishing scams, internet-based attacks, ransom ware, and malware.
Backup and Recovery
Backup and recovery are crucial aspects of data protection, ensuring that organizations can recover from data loss or damage. Backup and recovery involve creating and storing copies of data to protect against data loss, and then restoring that data to its original location or an alternate location where it can be used in place of the lost or damaged data.
Full Back up: A complete copy of the database, including data and transaction records, which takes a lot of time.
Transaction Log: Only the transaction logs are saved as the backup, keeping the backup file as small as possible.
Differential Backup: Similar to a full backup, but only the information that has changed since the last full backup is saved, resulting in smaller files.
Log-based Recovery: In this method, logs of all database transactions are stored in a secure area so that in case of a system failure, the database can recover the data.
Restore from Backup: This method involves restoring the data to its original location or an alternate location where it can be used in place of the lost or damaged data.
Data Dictionary Management
Data dictionary plays the most critical component of any database management system that is containing the essential information about the database’s files, their attributes, and data elements. It acts as the centralized repository of metadata, and offering the descriptions of data objects otherwise any items in data model that programmers and others can be easily referred to. The data dictionary enables with technical metadata, including data object names, definitions, reference data, missing data, properties, business rules for validation, and more.
The primary functions and benefits of a data dictionary include:
- Assisting in avoiding data inconsistencies across a project
- Defining conventions to be used across a project
- Providing consistency in the collection and use of data across multiple members of a research team
- Making data easier to analyse
- Enforcing the use of data standards
Query optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. It is an automated process that aims to determine the most efficient way to execute a given query by considering possible query plans.
The purpose of query optimization is to find the way to process a given query in minimum time; reducing the response time, preventing excessive resource consumption, and identifying poor query performance.
In DBMS, indexing is a concept that is going to use for optimizing the performance of any database by getting to minimize the number of disk accesses needed whenever a query is processed. Indexing involves creating a data structure, often a B-tree or a hash table, that holds the field value and a pointer to the record it relates to, allowing for faster data retrieval.
Data compression methods is implemented to decrease the size of data files otherwise records by getting to eliminate redundancies and irrelevancies in the data that is making it easier to store and most effective to transmit. Compression can be applied to tables, indexes, or partitions with CREATE, ALTER, and BACKUP commands.
Compression can be achieved in two primary ways: lossless and lossy. Lossless compression helps to reduce the size of data without getting to lose any information, whereas in lossy compression technique reduces the size of data by any scarification few information.
Data replication in a database management system (DBMS) is the process of storing data in more than one site or node, improving the availability of data and allowing multiple users to share the same data without inconsistency.
It is useful for enhancing fault tolerance, improving data locality, and simplifying backup and recovery processes. There are three main types of data replication: transactional, snapshot, and merge replication.
Data warehouse in a database management system (DBMS) is a central repository of integrated data from various sources, designed to support business intelligence (BI) activities, analytics, and decision-making processes. It allows aggregating a data from various sources into single, consistent data store; and getting to enable the organizations to execute the powerful analytics on enlarge volumes of historical data.
In data mining process, getting to search and analyse the enlarge sets of raw data to determine the patterns, relationships, and trends that helping out to solve business problems and making the informed decisions. It also enables the discovering and extracting patterns in enlarge data sets by using the methods at the intersection of database systems, machine learning, and statistics.
Data mining works as an interdisciplinary subfield in computer science and statistics; and overall goal of retrieving the information from the data set and transforming it into a flexible structure for further usage.
Data encryption in DBMS is a critical security measure that involves transforming data from plaintext (unencrypted) to cipher text (encrypted) to protect it from unauthorized access. Encryption ensures that only authorized users with the appropriate decryption key can access the data.
There are two main types of data encryption: symmetric encryption and asymmetric encryption.
Symmetric Encryption: This encryption method implements the similar cryptographic key for both encryption and decryption of the cipher text. It is most efficient for enlarge data sets and is commonly utilised for securing data at rest.
Asymmetric Encryption: Also known as Public-Key Cryptography, this method uses two separate cryptographic keys: a public key and a private key. The public key is implemented for encryption, whereas the private key is used for decryption. Asymmetric encryption is often utilised for securing data in transit and for digital signatures.
Scalability in a database management system (DBMS) refers to the ability of a database to handle increasing amounts of data, numbers of users, and types of requests without sacrificing performance or availability. Scalability is a critical factor in ensuring that a DBMS can meet the demands of growing applications and user bases.
There are two main types of database scalability: vertical scaling and horizontal scaling.
Vertical Scaling: This scaling method involves adding more resources, such as CPU, memory, or storage, to a single server to increase its capacity. Vertical scaling is often used for small to medium-sized databases and is limited by the maximum capacity of the server.
Horizontal Scaling: This scaling method involves adding more servers to a database system to increase its capacity. Horizontal scaling is often used for large-scale databases and can be achieved through techniques such as replication, partitioning, and sharding.
Other Characteristics of DBMS:
Data Independence: DBMS provides data independence by separating the physical storage details from the logical data representation.
Multi-user Support: DBMS allows multiple users to access and manipulate the data concurrently.
Cross-platform Compatibility: Many modern DBMS systems are designed to work on multiple operating systems and hardware platforms.
Data Validation and Default Values: DBMS supports the definition of validation rules and default values for columns to ensure data consistency.
Triggers and Stored Procedures: DBMS allows the creation of triggers and stored procedures, enabling the execution of predefined actions in response to specific events.
Data Backup and Recovery: DBMS systems offer features for regular data backups and efficient recovery mechanisms in case of failures.
Data Partitioning: DBMS supports the partitioning of large tables to improve performance and manageability.
Query Language Support: DBMS systems typically support a standardized query language such as SQL (Structured Query Language).
User-Friendly Interfaces: DBMS provides user interfaces for database administrators and end-users to interact with the database easily.
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