A quick review of the present need to store massive chunks of data relevant to multiple related or unrelated categories, reveals that databases must be highly effective at what they are designed to do.

This is not only because of the amount of data being continuously revised or modified that we are dealing with the dynamics of it aren’t of sole interest anymore. It’s because of the social value that every individual has assigned to them: databases are the literal backbone of a client’s lifestyle or a business’s worth.

Designing different types of databases lies at the core of the functionality that they provide to the users. Since data is a dynamic entity, the way it is stored varies a lot. It is also the reason behind companies designing their own types of databases that comply with their needs. In this article, we will be discussing the types of Databases in detail.

Types of Databases

There are several types of databases, that are briefly explained below.

• Hierarchical databases
• Network databases
• Object-oriented databases
• Relational databases
• Cloud Database
• Centralized Database
• Operational Database
• NoSQL databases

Hierarchical Databases

Just as in any hierarchy, this database follows the progression of data being categorized in ranks or levels, wherein data is categorized based on a common point of linkage. As a result, two entities of data will be lower in rank and the commonality would assume a higher rank. Refer to the diagram below:

A database is a collection of interrelated data that helps in the efficient retrieval, insertion, and deletion of data from the database and organizes the data in the form of tables, views, schemas, reports, etc. For Example, a university database organizes the data about students, faculty, admin staff, etc. which helps in the efficient retrieval, insertion, and deletion of data from it.

What is DBMS?

A Database Management System (DBMS) is a software system that is designed to manage and organize data in a structured manner. It allows users to create, modify, and query a database, as well as manage the security and access controls for that database. DBMS provides an environment to store and retrieve data in convenient and efficient manner.

Key Features of DBMS

• Data modeling: A DBMS provides tools for creating and modifying data models, which define the structure and relationships of the data in a database.
• Data storage and retrieval: A DBMS is responsible for storing and retrieving data from the database, and can provide various methods for searching and querying the data.
• Concurrency control: A DBMS provides mechanisms for controlling concurrent access to the database, to ensure that multiple users can access the data without conflicting with each other.
• Data integrity and security: A DBMS provides tools for enforcing data integrity and security constraints, such as constraints on the values of data and access controls that restrict who can access the data.
• Backup and recovery: A DBMS provides mechanisms for backing up and recovering the data in the event of a system failure.
• DBMS can be classified into two types: Relational Database Management System (RDBMS) and Non-Relational Database Management System (NoSQL or Non-SQL)
• RDBMS: Data is organized in the form of tables and each table has a set of rows and columns. The data are related to each other through primary and foreign keys.
• NoSQL: Data is organized in the form of key-value pairs, documents, graphs, or column-based. These are designed to handle large-scale, high-performance scenarios.

Types of DBMS

1. Relational Database Management System (RDBMS): Data is organized into tables (relations) with rows and columns, and the relationships between the data are managed through primary and foreign keys. SQL (Structured Query Language) is used to query and manipulate the data.
2. NoSQL DBMS: Designed for high-performance scenarios and large-scale data, NoSQL databases store data in various non-relational formats such as key-value pairs, documents, graphs, or columns.
3. Object-Oriented DBMS (OODBMS): Stores data as objects, similar to those used in object-oriented programming, allowing for complex data representations and relationships

Database Languages

• Data Definition Language
• Data Manipulation Language
• Data Control Language
• Transactional Control Language

Data Definition Language (DDL)

DDL is the short name for Data Definition Language, which deals with database schemas and descriptions, of how the data should reside in the database.

• CREATE: to create a database and its objects like (table, index, views, store procedure, function, and triggers)
• ALTER: alters the structure of the existing database
• DROP: delete objects from the database
• TRUNCATE: remove all records from a table, including all spaces allocated for the records are removed
• COMMENT: add comments to the data dictionary
• RENAME: rename an object

Data Manipulation Language (DML)

DML is the short name for Data Manipulation Language which deals with data manipulation and includes most common SQL statements such SELECT, INSERT, UPDATE, DELETE, etc., and it is used to store, modify, retrieve, delete and update data in a database. Data query language(DQL) is the subset of “Data Manipulation Language”. The most common command of DQL is SELECT statement. SELECT statement help on retrieving the data from the table without changing anything in the table.

• SELECT: retrieve data from a database
• INSERT: insert data into a table
• UPDATE: updates existing data within a table
• DELETE: Delete all records from a database table
• MERGE: UPSERT operation (insert or update)
• CALL: call a PL/SQL or Java subprogram
• EXPLAIN PLAN: interpretation of the data access path
• LOCK TABLE: concurrency Control

Data Control Language (DCL)

DCL is short for Data Control Language which acts as an access specifier to the database.(basically to grant and revoke permissions to users in the database

• GRANT: grant permissions to the user for running DML(SELECT, INSERT, DELETE,…) commands on the table
• REVOKE: revoke permissions to the user for running DML(SELECT, INSERT, DELETE,…) command on the specified table

Transactional Control Language (TCL)

TCL is short for Transactional Control Language which acts as an manager for all types of transactional data and all transactions. Some of the command of TCL are

• Roll Back: Used to cancel  or Undo changes made in the database
• Commit: It is used to apply or save changes in the database
• Save Point: It is used to save the data on the temporary basis in the database

Data Query Language (DQL)

Data query language(DQL) is the subset of “Data Manipulation Language”. The most common command of DQL is 1the SELECT statement. SELECT statement helps us in retrieving the data from the table without changing anything or modifying the table. DQL is very important for retrieval of essential data from a database.

Paradigm Shift from File System to DBMS

File System manages data using files on a hard disk. Users are allowed to create, delete, and update the files according to their requirements. Let us consider the example of file-based University Management System. Data of students is available to their respective Departments, Academics Section, Result Section, Accounts Section, Hostel Office, etc. Some of the data is common for all sections like Roll No, Name, Father Name, Address, and Phone number of students but some data is available to a particular section only like Hostel allotment number which is a part of the hostel office. Let us discuss the issues with this system:

• Redundancy of data: Data is said to be redundant if the same data is copied at many places. If a student wants to change their Phone number, he or she has to get it updated in various sections. Similarly, old records must be deleted from all sections representing that student.
• Inconsistency of Data: Data is said to be inconsistent if multiple copies of the same data do not match each other. If the Phone number is different in Accounts Section and Academics Section, it will be inconsistent. Inconsistency may be because of typing errors or not updating all copies of the same data.
• Difficult Data Access: A user should know the exact location of the file to access data, so the process is very cumbersome and tedious. If the user wants to search the student hostel allotment number of a student from 10000 unsorted students’ records, how difficult it can be.
• Unauthorized Access: File Systems may lead to unauthorized access to data. If a student gets access to a file having his marks, he can change it in an unauthorized way.
• No Concurrent Access: The access of the same data by multiple users at the same time is known as concurrency. The file system does not allow concurrency as data can be accessed by only one user at a time.
• No Backup and Recovery: The file system does not incorporate any backup and recovery of data if a file is lost or corrupted.

These are the main reasons which made a shift from file system to DBMS. Also See, Advantages of DBMS over File System

Advantages of DBMS

• Data organization: A DBMS allows for the organization and storage of data in a structured manner, making it easy to retrieve and query the data as needed.
• Data integrity: A DBMS provides mechanisms for enforcing data integrity constraints, such as constraints on the values of data and access controls that restrict who can access the data.
• Concurrent access: A DBMS provides mechanisms for controlling concurrent access to the database, to ensure that multiple users can access the data without conflicting with each other.
• Data security: A DBMS provides tools for managing the security of the data, such as controlling access to the data and encrypting sensitive data.
• Backup and recovery: A DBMS provides mechanisms for backing up and recovering the data in the event of a system failure.
• Data sharing: A DBMS allows multiple users to access and share the same data, which can be useful in a collaborative work environment.

Disadvantages of DBMS

• Complexity: DBMS can be complex to set up and maintain, requiring specialized knowledge and skills.
• Performance overhead: The use of a DBMS can add overhead to the performance of an application, especially in cases where high levels of concurrency are required.
• Scalability: The use of a DBMS can limit the scalability of an application, since it requires the use of locking and other synchronization mechanisms to ensure data consistency.
• Cost: The cost of purchasing, maintaining and upgrading a DBMS can be high, especially for large or complex systems.
• Limited Use Cases: Not all use cases are suitable for a DBMS, some solutions don’t need high reliability, consistency or security and may be better served by other types of data storage.

Applications of DBMS

• Enterprise Information: Sales, accounting, human resources, Manufacturing, online retailers.
• Banking and Finance Sector: Banks maintaining the customer details, accounts, loans, banking transactions, credit card transactions. Finance: Storing the information about sales and holdings, purchasing of financial stocks and bonds.
• University: Maintaining the information about student course enrolled information, student grades, staff roles.
• Airlines: Reservations and schedules.
• Telecommunications: Prepaid, postpaid bills maintance.

Conclusion

A Database Management System (DBMS) is an essential tool for efficiently managing, organizing, and retrieving large volumes of data across various industries. Its ability to handle data securely, ensure integrity, support concurrent access, and provide backup and recovery options makes it indispensable for modern data-driven applications. While DBMSs come with complexities and costs, their benefits in terms of data management and security far outweigh the challenges, making them a crucial component in any data-centric environment

Data is a collection of facts and figures. The data collection was increasing day to day and they needed to be stored in a device or a safer software. Charles Bachman was the first person to develop the Integrated Data Store (IDS) which was based on a network data model for which he was inaugurated with the Turing Award (The most prestigious award which is equivalent to the Nobel prize in the field of Computer Science.). It was developed in the early 1960s. In the late 1960s, IBM (International Business Machines Corporation) developed the Integrated Management Systems which is the standard database system used to date in many places. It was developed based on the hierarchical database model. It was during the year 1970 that the

relational database model

was developed by Edgar Codd. Many of the database models we use today are relational-based. It was considered the standardized database model from then. The relational model was still in use by many people in the market. Later during the same decade (1980’s), IBM developed the

Structured Query Language (SQL)

as a part of the R project. It was declared as a standard language for the queries by ISO and ANSI. The Transaction Management System for processing transactions was also developed by James Gray for which he was felicitated the Turing Award. Further, there were many other models with rich features like complex queries, datatypes to insert images, and many others. The Internet Age has perhaps influenced the data models much more. Data models were developed using object-oriented programming features, embedding with scripting languages like

Hyper Text Markup Language (HTML)

for queries. With humongous data being available online, DBMS is gaining more significance day by day.

Techniques for data storage and processing have evolved over the years:

• 1950s and early 1960s: Magnetic tapes were developed for data storage. Data processing tasks such as payroll were automated, with data stored on tapes. Processing of data consisted of reading data from one or more tapes and writing data to a new tape. Data could also be input from punched card decks, and output to printers. For example, salary raises were processed by entering the raises on punched cards and reading the punched card deck in synchronization with a tape containing the master salary details. The records had to be in the same sorted order. The salary raises would be added to the salary read from the master tape, and written to a new tape; the new tape would become the new master tape. Tapes (and card decks) could be read only sequentially, and data sizes were much larger than main memory; thus, data processing programs were forced to process data in a particular order, by reading and merging data from tapes and card decks.
• Late 1960s and 1970s: Widespread use of hard disks in the late 1960s changed the scenario for data processing greatly, since hard disks allowed direct access to data. The position of data on disk was immaterial, since any location on disk could be accessed in just tens of milliseconds. Data were thus freed from the tyranny of sequentiality. With disks, network and hierarchical databases could be created that allowed data structures such as lists and trees to be stored on disk. Programmers could construct and manipulate these data structures. A landmark paper by Codd [1970] defined the relational model and nonprocedural ways of querying data in the relational model, and relational databases were born. The simplicity of the relational model and the possibility of hiding implementation details completely from the programmer were enticing indeed. Codd later won the prestigious Association of Computing Machinery Turing Award for his work.
• 1980s: Although academically interesting, the relational model was not used in practice initially, because of its perceived performance disadvantages; relational databases could not match the performance of existing network and hierarchical databases. That changed with System R, a groundbreaking project at IBM Research that developed techniques for the construction of an efficient relational database system. Excellent overviews of System R are provided by Astrahan et al. [1976] and Chamberlin et al. [1981]. The fully functional System R prototype led to IBM’s first relational database product, SQL/DS. At the same time, the Ingres system was being developed at the University of California at Berkeley. It led to a commercial product of the same name. Initial commercial relational database systems, such as IBM DB2, Oracle, Ingres, and DEC Rdb, played a major role in advancing techniques for efficient processing of declarative queries. By the early 1980s, relational databases had become competitive with network and hierarchical database systems even in the area of performance. Relational databases were so easy to use that they eventually replaced network and hierarchical databases; programmers using such databases were forced to deal with many low-level implementation details, and had to code their queries in a procedural fashion. Most importantly, they had to keep efficiency in mind when designing their programs, which involved a lot of effort. In contrast, in a relational database, almost all these low-level tasks are carried out automatically by the database, leaving the programmer free to work at a logical level. Since attaining dominance in the 1980s, the relational model has reigned supreme among data models. The 1980s also saw much research on parallel and distributed databases, as well as initial work on object-oriented databases.
• Early 1990s: The SQL language was designed primarily for decision support applications, which are query-intensive, yet the mainstay of databases in the 1980s was transaction-processing applications, which are update-intensive. Decision support and querying re-emerged as a major application area for databases. Tools for analyzing large amounts of data saw large growths in usage. Many database vendors introduced parallel database products in this period. Database vendors also began to add object-relational support to their databases.
• 1990s: The major event of the 1990s was the explosive growth of the World Wide Web. Databases were deployed much more extensively than ever before. Database systems now had to support very high transaction-processing rates, as well as very high reliability and 24 × 7 availability (availability 24 hours a day, 7 days a week, meaning no downtime for scheduled maintenance activities). Database systems also had to support Web interfaces to data.
• 2000s: The first half of the 2000s saw the emerging of XML and the associated query language XQuery as a new database technology. Although XML is widely used for data exchange, as well as for storing certain complex data types, relational databases still form the core of a vast majority of large-scale database applications. In this time period we have also witnessed the growth in “autonomic-computing/auto-admin” techniques for minimizing system administration effort. This period also saw a significant growth in use of open-source database systems, particularly PostgreSQL and MySQL. The latter part of the decade has seen growth in specialized databases for data analysis, in particular column-stores, which in effect store each column of a table as a separate array, and highly parallel database systems designed for analysis of very large data sets. Several novel distributed data-storage systems have been built to handle the data management requirements of very large Web sites such as Amazon, Facebook, Google, Microsoft and Yahoo!, and some of these are now offered as Web services that can be used by application developers. There has also been substantial work on management and analysis of streaming data, such as stock-market ticker data or computer network monitoring data. Data-mining techniques are now widely deployed; example applications include Web-based product-recommendation systems and automatic placement of relevant advertisements on Web pages.

The efficient and safe management, saving and retrieval of data is made possible by the Database Management Systems. They provide strong solutions for the data management demands and are the foundation of the numerous applications used in a variety of the sectors. Recognizing the uses of DBMSs aids in understanding their significance in contemporary company operations and technology.

What is DBMS?

A Database Management System (DBMS) makes easier to create, maintain and work with the databases. It acts as the channel between end users and the database enabling the functions including administration, retrieval, updating and storing of data. By structuring data into the organized formats and controlling concurrent access the database management systems (DBMSs) contribute to the efficient handling, security and integrity of data.

There are different fields where a database management system is utilized. Following are a few applications that utilize the information base administration framework.

1. Railway Reservation System

In the rail route reservation framework, the information base is needed to store the record or information of ticket appointments, status of train’s appearance, and flight. Additionally, if trains get late, individuals become acquainted with it through the information base update.

2. Library Management System

There are many books in the library so; it is difficult to store the record of the relative multitude of books in a register or duplicate. Along these lines, the data set administration framework (DBMS) is utilized to keep up all the data identified with the name of the book, issue date, accessibility of the book, and its writer.

3. Banking

Database the executive’s framework is utilized to store the exchange data of the client in the information base.

4. Education Sector

Presently, assessments are led online by numerous schools and colleges. They deal with all assessment information through the data set administration framework (DBMS). In spite of that understudy’s enlistments subtleties, grades, courses, expense, participation, results, and so forth all the data is put away in the information base.

5. Credit card exchanges

The database Management framework is utilized for buying on charge cards and age of month to month proclamations.

6. Social Media Sites

We all utilization of online media sites to associate with companions and to impart our perspectives to the world. Every day, many people group pursue these online media accounts like Pinterest, Facebook, Twitter, and Google in addition to. By the utilization of the data set administration framework, all the data of clients are put away in the information base and, we become ready to interface with others.

7. Broadcast communications

Without DBMS any media transmission organization can’t think. The Database the executive’s framework is fundamental for these organizations to store the call subtleties and month to month postpaid bills in the information base.

8. Accounting and Finance

The information base administration framework is utilized for putting away data about deals, holding and acquisition of monetary instruments, for example, stocks and bonds in a data set.

9. E-Commerce Websites

These days, web-based shopping has become a major pattern. Nobody needs to visit the shop and burn through their time. Everybody needs to shop through web based shopping sites, (for example, Amazon, Flipkart, Snapdeal) from home. So all the items are sold and added uniquely with the assistance of the information base administration framework (DBMS). Receipt charges, installments, buy data these are finished with the assistance of DBMS.

10. Human Resource Management

Big firms or organizations have numerous specialists or representatives working under them. They store data about worker’s compensation, assessment, and work with the assistance of an information base administration framework (DBMS).

11. Manufacturing

Manufacturing organizations make various kinds of items and deal them consistently. To keep the data about their items like bills, acquisition of the item, amount, inventory network the executives, information base administration framework (DBMS) is utilized.

12. Airline Reservation System

This framework is equivalent to the railroad reservation framework. This framework additionally utilizes an information base administration framework to store the records of flight takeoff, appearance, and defer status.

13. Healthcare System

DBMS is used in healthcare to manage patient data, medical records, and billing information.

14. Security

DBMS provides security features to ensure that only authorized users have access to the data.

15. Telecommunication

Database Management Systems (DBMS) are essential to the telecommunications industry because they manage enormous volumes of data on billing, customer information, and network optimization.

Conclusion

In Conclusion, database management systems, or DBMS, are essential to effective, safe, and well-organized data management. They offer multiple user access, enable scalability, and guarantee integrity. Data recovery, decision support, and web-based platforms are just a few of the uses for database management systems (DBMS) that are essential to contemporary information systems and satisfy a wide range of business objectives.

A Data Base Management System is a system software for easy, efficient and reliable data processing and management. It can be used for:

• Creation of a database.
• Retrieval of information from the database.
• Updating the database.
• Managing a database.
  • Multiple User Interface
  • Data scalability, expandability and flexibility: We can change schema of the database, all schema will be updated according to it.
  • Overall the time for developing an application is reduced.
  • Security: Simplifies data storage as it is possible to assign security permissions allowing restricted access to data.

Data organization: DBMS allow users to organize large amounts of data in a structured and systematic way. Data is organized into tables, fields, and records, making it easy to manage, store, and retrieve information.

Data scalability: DBMS are designed to handle large amounts of data and are scalable to meet the growing needs of organizations. As organizations grow, DBMS can scale up to handle increasing amounts of data and user traffic.

we will discuss the need for a DBMS in detail, covering the following points:

1.Data Organization and Management

2.Data Security and Privacy

3.Data Integrity and Consistency

4.Concurrent Data Access

5.Data Analysis and Reporting

6.Scalability and Flexibility

7.Cost-Effectiveness

1. Data Organization and Management:
One of the primary needs for a DBMS is data organization and management. DBMSs allow data to be stored in a structured manner, which helps in easier retrieval and analysis. A well-designed database schema enables faster access to information, reducing the time required to find relevant data. A DBMS also provides features like indexing and searching, which make it easier to locate specific data within the database. This allows organizations to manage their data more efficiently and effectively.

2. Data Security and Privacy:
DBMSs provide a robust security framework that ensures the confidentiality, integrity, and availability of data. They offer authentication and authorization features that control access to the database. DBMSs also provide encryption capabilities to protect sensitive data from unauthorized access. Moreover, DBMSs comply with various data privacy regulations such as the GDPR, HIPAA, and CCPA, ensuring that organizations can store and manage their data in compliance with legal requirements.

3. Data Integrity and Consistency:
Data integrity and consistency are crucial for any database. DBMSs provide mechanisms that ensure the accuracy and consistency of data. These mechanisms include constraints, triggers, and stored procedures that enforce data integrity rules. DBMSs also provide features like transactions that ensure that data changes are atomic, consistent, isolated, and durable (ACID).

4. Concurrent Data Access:
A DBMS provides a concurrent access mechanism that allows multiple users to access the same data simultaneously. This is especially important for organizations that require real-time data access. DBMSs use locking mechanisms to ensure that multiple users can access the same data without causing conflicts or data corruption.

5. Data Analysis and Reporting:
DBMSs provide tools that enable data analysis and reporting. These tools allow organizations to extract useful insights from their data, enabling better decision-making. DBMSs support various data analysis techniques such as OLAP, data mining, and machine learning. Moreover, DBMSs provide features like data visualization and reporting, which enable organizations to present their data in a visually appealing and understandable way.

6. Scalability and Flexibility:
DBMSs provide scalability and flexibility, enabling organizations to handle increasing amounts of data. DBMSs can be scaled horizontally by adding more servers or vertically by increasing the capacity of existing servers. This makes it easier for organizations to handle large amounts of data without compromising performance. Moreover, DBMSs provide flexibility in terms of data modeling, enabling organizations to adapt their databases to changing business requirements.

7. Cost-Effectiveness:
DBMSs are cost-effective compared to traditional file-based systems. They reduce storage costs by eliminating redundancy and optimizing data storage. They also reduce development costs by providing tools for database design, maintenance, and administration. Moreover, DBMSs reduce operational costs by automating routine tasks and providing self-tuning capabilities.

In the area of database management, the data is arranged in two ways which are Relational Databases (SQL) and Non-Relational Databases (NoSQL). While relational databases organize data into structured tables, non-relational databases use various flexible data models like key-value pairs, documents, graphs, and wide–column stores.

Here, we will learn about non-relational database meaning and check non-relational database examples. But to understand non-relational databases, or “NoSQL” databases, we first need to look at relational databases.

Relational Database (SQL)

• A relational database stores data in a table composed of rows and columns. The table represents an object or entity, such as users, customers, orders, etc.
• The column represents the type of data that can be stored in the respective column.
• Relational Databases allow users to establish a connection between tables using keys for flexible data flow and querying.
• SQL was specifically designed to work with tabular data. These are often categorized as structured data.
• This is because there can only be a single schema or structure for the data within a relational database.
• SQL is a declarative language, which means that you describe in SQL syntax the desired result you wish from the query.

Key features of relational database

• Relational data models are similar to an Excel spreadsheet, with related data stored in rows and columns in one table.
• SQL (Structured Query Language) is the most common way of interacting with relational database systems. Developers can use SQL queries to execute CRUD (Create, Read, Update, Delete) operations.

Non-Relational database (NoSQL)

• Non-relational databases different from relational databases because they do not store data in tabular form.
• Instead, non-relational databases are based on data structures like documents and graphs. NoSQL databases also come in a variety of types based on their data models.
• They offer scalability when dealing with large volumes of data and high load factors. They were designed when data was expected to be partitioned across multiple machines to scale, in contrast to relational databases, which assumed the data would stay on a single machine.

The benefits of a non-relational database

• Scalability: Non-relational databases like MongoDB and Cassandra are designed to horizontally scale across clusters of cheap commodity hardware, offering seamless scalability as data volumes and user loads increase.
• Flexibility in Data Models: Unlike rigid table-based structures in relational databases, non-relational databases support flexible data models like document stores (e.g., JSON in MongoDB), key-value pairs (e.g., Redis), and wide-column stores (e.g., Cassandra), making it easier to store and manage unstructured or semi-structured data.
• Performance: Non-relational databases are optimized for specific use cases such as real-time data ingestion, high-speed transactions, and rapid access to large volumes of data. They often outperform relational databases in these scenarios due to their distributed architecture and optimized data storage formats.
• Schemaless Design: Non-relational databases typically do not enforce a rigid schema, allowing developers to evolve the data structure over time without downtime or complex migrations. This advantage is particularly beneficial in agile development environments and for handling diverse and unpredictable data types.
• High Availability and Fault Tolerance: Many non-relational databases are designed with built-in replication and automatic failover capabilities, ensuring high availability and data redundancy. This makes them suitable for mission-critical applications where continuous uptime is essential.
• Cost-Effectiveness: By using commodity hardware and open-source software, non-relational databases often provide a more cost-effective solution compared to traditional relational databases, especially at scale.

What do NoSQL databases have in common?

• Non-Relational Structure: NoSQL databases store data in flexible formats like key-value pairs, documents, or graphs, allowing for easier adaptation to changing data needs.
• Scalability: It is Designed for horizontal scaling across multiple servers, enabling efficient handling of large data volumes and high transaction rates.
• High Performance: It is Highly Optimized for specific query types and workloads, prioritizing low latency and high throughput.
• Flexibility in Data Models: It Supports various data structures (e.g., documents, columns, graphs) to fit diverse application requirements without rigid schemas.
• Eventual Consistency: It Emphasizes availability and partition tolerance over strict immediate consistency across distributed nodes.
• Horizontal Partitioning: It Uses sharding to distribute data across multiple nodes, improving performance and managing large datasets efficiently.

Non-Relational Database Types

There are four main types of non-relational databases:

• key/value
• graph
• column
• document

1. Key/Value Database

Key-value databasesuse a straightforward schema: a unique key is paired with a collection of values, where the values can be anything from a string to a large binary object. One of the benefits of using this structure in a database is that you don’t have to worry about complex queries. Because the system knows where the data is stored, it only sends a request to that particular server.

Example of Key/Value Database

2. Graph Database

Graph database is another type of non-relational database. A popular example of a graph database is Neo4J. This database stores information as a collection of nodes and edges, where the edges represent the relationships between the nodes.

3. Column Oriented Database

A column-oriented or wide-column non-relational database is primarily designed for analytics. Cassandra is a commonly used column-oriented database.

The advantage of column-oriented/row-oriented databases is that column-oriented databases return data in columns, making the query much more performant as it will not return many irrelevant fields that are not required for the query being serviced.

The primary key in a column-oriented database is the data or value, which is then mapped to row keys. This is the inverse, or opposite, of how the primary key works in a relational database.

Example of Column Oriented Database

4. Document Database

Document databases, such as MongoDB, store data in a single document, which can have different shapes within the single collection or table that stores the documents. It provides a clear means of capturing relationships using sub-documents and embedded arrays within a single document.

Example of Document Database

Non-Relational Database Management Systems

Some of the popular Non-Relational Database Management Systems are:

1. MongoDB
2. Apache Cassandra
3. Redis
4. Couchbase
5. Apache HBase
6. Neo4j
7. Riak
8. Aerospike
9. OrientDB
10. ArangoDB

These are some Non-relational database names, that you might hear in the market. Decide on which Non-relational database software is best for your work, and master that.

Relational vs Non-Relational Database

Here’s a comparison of Relational and Non-Relational Databases in tabular format:

Conclusion

In conclusion, the choice between relational and non-relational databases depends largely on the nature of the data and the requirements of the application. Relational databases excel in structured data environments where data integrity and complex querying are paramount. On the other hand, non-relational databases shine in scenarios demanding scalability, flexibility in data models, and high performance across distributed systems.

Structured Query Language (SQL) is a specialized programming language for managing relational database data. It allows users to store, manipulate, and retrieve data efficiently in databases like MySQL, SQL Server, Oracle, and more.

In this article, we will learn about what is SQL? and check its characteristics, rules, uses, commands, etc.

What is SQL?

SQL stands for Structured Query Language. SQL is a computer language used to interact with relational database systems. SQL is a tool for organizing, managing, and retrieving archived data from a computer database.

When data needs to be retrieved from a database, SQL is used to make the request. The DBMS processes the SQL query retrieves the requested data and returns it to us. Rather, SQL statements describe how a collection of data should be organized or what data should be extracted or added to the database.

In common usage, SQL encompasses DDL and DML commands for CREATE, UPDATE, MODIFY, or other operations on database structure.

SQL History

• SQL was invented in 1970s and was first commercially distributed by Oracle.
• The original name was given by IBM as Structured English Query Language, abbreviated by the acronym SEQUEL.

Components of a SQL System

Some of the Key components of a SQL System are:

Databases

Databases are structured collections of data organized into tables, rows, and columns. They serve as repositories for storing information efficiently and provide a way to manage and access data.

Tables

Tables are the fundamental building blocks of a database, consisting of rows (records) and columns (attributes or fields). They ensure data integrity and consistency by defining the structure and relationships of the stored information.

Queries

Queries are SQL commands used to interact with databases. They enable users to retrieve, update, insert, or delete data from tables, allowing for efficient data manipulation and retrieval.

Constraints

Constraints are rules applied to tables to maintain data integrity. They define conditions that data must meet to be stored in the database, ensuring accuracy and consistency.

Stored Procedures

Stored procedures are pre-compiled SQL statements stored in the database. They can accept parameters, execute complex operations, and return results, enhancing efficiency, reusability, and security in database management.

Transactions

Transactions are groups of SQL statements that are executed as a single unit of work. They ensure data consistency and integrity by allowing for the rollback of changes if any part of the transaction fails.

Some other important components include:

• Data Types
• Indexes
• Views
• Security and Permissions
• Joins

What are the characteristics of SQL?

• SQL may be utilized by quite a number of users, which include people with very little programming experience.
• SQL is a non-procedural language.
• We can without difficulty create and replace databases in SQL. It isn’t a time-consuming process.
• SQL is primarily based totally on ANSI standards.
• SQL does now no longer have a continuation individual.
• SQL is entered into the SQL buffer on one or more lines.
• SQL makes use of a termination individual to execute instructions immediately. It makes use of features to carry out a few formatting.
• It uses functions to perform some formatting.

How SQL Works?

A server machine is used in the implementation of the structured query language (SQL), processing database queries and returning results. The following are some of the software elements that the SQL process goes through.

Parser

The parser begins by replacing some of the words in the SQL statement with unique symbols, a process known as tokenization. The statement is then examined for the following:

Correctness

The parser checks to see if the SQL statement complies with the rules, or SQL semantics, that guarantee the query statement’s accuracy. The parser, for instance, looks to see if the SQL command ends with a semicolon. The parser returns an error if the semi-colon is absent.

Authorization

The parser additionally confirms that the user executing the query has the required permissions to alter the relevant data.

Relational Engine

The relational engine, also known as the query processor, develops a strategy for efficiently retrieving, writing, or updating relevant data. For instance, it looks for queries that are similar to others, uses earlier data manipulation techniques, or develops a new one. Byte code, an intermediate-level representation of the SQL statement, is used to write the plan. To efficiently perform database searches and modifications, relational databases use byte code.

Storage Engine

The software element that interprets the byte code and executes the intended SQL statement is known as the storage engine, also known as the database engine. The data in the database files on the physical disc storage is read and stored. The storage engine delivers the outcome to the requesting application after completion.

SQL Rules

The rules for writing SQL queries are given below:

• A ‘;’ is used to end SQL statements.
• Statements may be split across lines, but keywords may not.
• Identifiers, operator names, and literals are separated by one or more spaces or other delimiters.
• A comma (,) separates parameters without a clause.
• A space separates a clause.
• Reserved words cannot be used as identifiers unless enclosed with double quotes.
• Identifiers can contain up to 30 characters.
• Identifiers must start with an alphabetic character.
• Characters and date literals must be enclosed within single quotes.
• Numeric literals can be represented by simple values.
• Comments may be enclosed between /* and */ symbols and maybe multi-line.

What are SQL commands?

Developers use structured query language (SQL) commands, which are specific keywords or SQL statements, to work with data stored in relational databases. The following are categories for SQL commands.

1. Data Definition Language

SQL commands used to create the database structure are known as data definition language (DDL). Based on the needs of the business, database engineers create and modify database objects using DDL. The CREATE command, for instance, is used by the database engineer to create database objects like tables, views, and indexes.

2. Data Manipulation Language

A relational database can be updated with new data using data manipulation language (DML) statements. The INSERT command, for instance, is used by an application to add a new record to the database.

3. Data Query Language

Data retrieval instructions are written in the data query language (DQL), which is used to access relational databases. The SELECT command is used by software programs to filter and return particular results from a SQL table.

4. Data Control language

Data control language (DCL) is a programming language used by database administrators to control or grant other users access to databases. For instance, they can allow specific applications to manipulate one or more tables by using the GRANT command.

5. Transaction Control Language

To automatically update databases, the relational engine uses transaction control language (TCL). For instance, the database can reverse a mistaken transaction using the ROLLBACK command.

Uses of SQL

SQL is used for interacting with databases. These interactions include:

1. Data definition: It is used to define the structure and organization of the stored data and the relationships among the stored data items.
2. Data retrieval: SQL can also be used for data retrieval.
3. Data manipulation: If the user wants to add new data, remove data, or modifying in existing data then SQL provides this facility also.
4. Access control: SQL can be used to restrict a user’s ability to retrieve, add, and modify data, protecting stored data against unauthorized access.
5. Data sharing: SQL is used to coordinate data sharing by concurrent users, ensuring that changes made by one user do not inadvertently wipe out changes made at nearly the same time by another user.

SQL also differs from other computer languages because it describes what the user wants the computer to do rather than how the computer should do it. (In more technical terms, SQL is a declarative or descriptive language rather than a procedural one.)

SQL contains no IF statement for testing conditions, and no GOTO, DO, or FOR statements for program flow control. Rather, SQL statements describe how a collection of data is to be organized, or what data is to be retrieved or added to the database. The sequence of steps to do those tasks is left for the DBMS to determine.

Why SQL?

• SQL is an interactive question language. Users type SQL instructions into an interactive SQL software to retrieve facts and show them on the screen, presenting a convenient, easy-to-use device for ad hoc database queries.
• SQL is a database programming language. Programmers embed SQL instructions into their utility packages to access the facts in a database. Both user-written packages and database software packages (consisting of document writers and facts access tools) use this approach for database access.
• SQL is a client/server language. Personal computer programs use SQL to communicate over a network with database servers that save shared facts. This client/server architecture is utilized by many famous enterprise-class applications.
•  SQL is Internet facts access language. Internet net servers that interact with company facts and Internet utility servers all use SQL as a widespread language for getting access to company databases, frequently through embedding SQL databases get entry to inside famous scripting languages like PHP or Perl.
•  SQL is a distributed database language. Distributed database control structures use SQL to assist distribute facts throughout many linked pc structures. The DBMS software program on every gadget makes use of SQL to speak with the opposite structures, sending requests for facts to get entry to.
• SQL is a database gateway language. In a pc community with a mixture of various DBMS products, SQL is frequently utilized in a gateway that lets one logo of DBMS speak with every other logo. SQL has for this reason emerged as a useful, effective device for linking people, pc packages, and pc structures to the facts saved in a relational database.

SQL Injection

A cyberattack known as SQL injection involves tricking the database with SQL queries. To retrieve, alter, or corrupt data in a SQL database, hackers use SQL injection. To execute a SQL injection attack, for instance, they might enter a SQL query in place of a person’s name in a submission form.

What is SQL Server?

Microsoft’s relational database management system, which uses SQL to manipulate data, is formally known as SQL Server. There are various editions of the MS SQL Server, and each is tailored for particular workloads and requirements.

Finally, SQL is not a particularly structured language, especially when compared with highly structured languages such as C, Pascal, or Java. Instead, SQL statements resemble English sentences, complete with “noise words” that don’t add to the meaning of the statement but make it read more naturally. SQL has quite a few inconsistencies and also some special rules to prevent you from constructing SQL statements that look perfectly legal but that don’t make sense.

Despite the inaccuracy of its name, SQL has emerged as the standard language for using relational databases. SQL is both a powerful language and one that is relatively easy to learn. So, SQL is a database management language. The database administrator is answerable for handling a minicomputer or mainframe database and makes use of SQL to outline the database shape and manipulate get entry to the saved data.

Conclusion

SQL(Structured Query Language) is a programming language designed for managing and manipulating data stored in relational databases. It is used for interacting with DBMS like MySQL, SQL Server, Oracle, and PostgreSQL.