Difference File System and DBMS.

Two common approaches to storing and managing data are File Systems and Database Management Systems (DBMS). While both serve the purpose of data storage, they differ vastly in their structure, functionality, and usability. This article will cover the core differences between File Systems and DBMS in simple terms with examples.

What is a File System?

A File System is a method used by operating systems to store, organize, and manage files on storage devices like hard drives, SSDs, and USB drives. It arranges data into files and directories, allowing users to read, write, edit, and delete information.

Key Characteristics of a File System:

• Stores data in files and folders.
• Handles basic operations like create, read, write, and delete.
• No built-in support for complex relationships or querying like in databases.

Example: Saving a document in Microsoft Word or storing photos in a folder on your computer utilizes a file system like NTFS (Windows) or ext4 (Linux). The operating system keeps track of where each file is located and how to access it.

What is a DBMS?

A DBMS (Database Management System) is software that allows users to create, manage, and interact with databases. It provides tools to store, retrieve, modify, and secure data efficiently, especially when dealing with large volumes or complex relationships.

Key Characteristics of a DBMS:

• Organizes data in tables (rows and columns).
• Supports querying using languages like SQL.
• Ensures data integrity, security, and consistency.
• Supports multiple users accessing data simultaneously.

Example: When an e-commerce website stores customer orders, product details, and payment info in an organized way so it can retrieve or update them through SQL queries—that's done using a DBMS.

Difference Between File System and DBMS.

Here we are listing the key differences between the File System and a DBMS:

File System	DBMS (Database Management System)
Stores data in files and folders manually.	Organizes data in structured tables with rows and columns.
High redundancy; the same data may be stored in multiple files.	Reduces redundancy using normalization and relationships.
Hard to maintain consistency across multiple files.	Ensures data consistency through integrity constraints and transactions.
Basic file-level security.	Advanced security with access control, user authentication, and roles.
Manual or via basic file handling programs.	Accessed using powerful query languages like SQL.
Slower and less efficient for large data.	Fast and optimized using indexes and optimized query engines.
Difficult to manage multiple users simultaneously	Supports multiple users with proper concurrency control mechanisms.
Must be handled manually.	Automatic backup and recovery features available.
Depends on the programmer to maintain.	Enforced through constraints like Primary Key, Foreign Key, etc.
No built-in support for relationships between data.	Supports complex relationships using keys and joins.
Not scalable for large data.	Highly scalable and supports large databases.
Example: Notepad, CSV files, Excel	Example: MySQL, PostgreSQL, Oracle, SQL Server

While file systems are suitable for simple data storage, a DBMS is essential for managing complex, large-scale, and multi-user databases with better security, consistency, and performance.

Data Independence (Logical and Physical).

Data Independence is a fundamental concept in Database Management Systems (DBMS) that ensures changes made at one level of a database do not affect the other levels. It helps maintain data integrity, flexibility, and efficiency, especially as databases grow and evolve.

What is Data Independence?

Data Independence refers to the capacity to change the schema at one level of a database system (e.g., storage or structure) without altering the schema at the next higher level (e.g., user views or applications). It enables abstraction between how data is stored, how it’s structured, and how users interact with it.

There are two types of data independence:

• Logical Data Independence
• Physical Data Independence

Logical Data Independence.

Logical Data Independence is the ability to change the conceptual structure of the database (like adding or modifying tables, columns, or relationships) without changing the external structure of how users or applications access the data.

Example: Let’s say a column PhoneNumber is added to the Student table. If the user views don’t need this column, no changes are needed in how users query the database.

Key Benefits of Logical Data Independence:

• Helps in restructuring tables, adding new fields, or merging data without disturbing users.
• Supports evolving business logic and entity relationships.
• Crucial for large-scale applications with multiple user roles and views.

How to achieve Logical Data Independence?

• Use of  Views: Applications and users interact with views that are mapped to the logical schema. Changes to the schema can be managed behind the scenes without changing the views.

• Abstraction through External Schema: The external schema defines what data the user sees, not how it's structured. So, even if the underlying logical model changes, the view stays the same.

• DBMS Support: Most modern DBMS systems support logical data independence through metadata management, view mechanisms, and query rewriting.

Physical Data Independence.

Physical Data Independence is the ability to change (internal schema) how the data is stored internally (like changing file structures, indexes, or compression methods) without affecting (conceptual schema) how the data is accessed by users or applications.

Example: If you change the way data is stored (e.g., from heap storage to B-tree indexing for faster access), users and applications don’t need to modify their queries.

Key Benefits of Physical Independence:

• Allows performance tuning and storage optimization.
• Ensures the database remains efficient even if hardware or indexing strategies change.
• Makes data migration and infrastructure upgrades easier.

How to achieve Physical Data Independence?

• DBMS Abstraction Layer: The DBMS acts as an abstraction layer between the physical storage and the logical schema. It translates user queries into low-level storage operations, shielding users from physical changes.
• Metadata Management: Information about data storage (like indexing methods, file locations, and compression) is stored in metadata. This allows physical changes without modifying logical definitions.
• Use of Indexes and Storage Techniques: You can add indexes, change data block sizes, or switch to SSDs without altering tables or affecting application queries.

Difference Between Logical and Physical Data Independence.

Logical Data Independence	Physical Data Independence
Ability to change the logical schema without changing the external views or applications.	Ability to change the physical storage without altering the logical schema or applications.
Protects user views and application programs from changes in logical structure.	Protects the logical schema and application from changes in physical data storage.
Adding a new column or table does not affect existing user views.	Changing data file format or indexing does not affect logical structure or queries.
Harder to achieve as applications depend heavily on the logical structure.	Easier to achieve as internal changes are hidden by the DBMS engine.
Useful during database redesigns, data model changes, or schema upgrades.	Useful during performance optimization, storage upgrades, or disk reorganization.
Logical changes may affect multiple applications if independence is not maintained.	Physical changes rarely affect applications if independence is well-implemented.
Requires high-level data abstraction and flexible application design.	Achieved through DBMS internals like metadata, indexes, and query optimization layers.

Conclusion.

Data independence is crucial in modern database systems to ensure flexibility, security, and efficient data management. By separating the physical, logical, and user layers, developers can modify, scale, or optimize databases without disrupting applications or user experiences. Mastering Logical and Physical Data Independence is essential for any database administrator or backend developer.

Three Schema Architecture in DBMS.

Database systems are complex. To manage this complexity, the Three-Schema Architecture in DBMS provides a structured approach that separates user interactions, logical design, and physical storage. This architecture enhances data abstraction, security, and maintainability. 

What is Three Schema Architecture in DBMS?

The Three Schema Architecture is a framework used in database systems to separate the user view, logical design, and internal storage of data. It consists of three layers:

1. External Schema (View Level)

2. Conceptual Schema (Logical Level)

3. Internal Schema (Physical Level)

This separation ensures that changes in one layer do not impact the others, providing flexibility and better control over data management.

External Schema (View Level).

The External Schema, also known as the View Level, is the topmost layer of the Three-Schema Architecture in a DBMS. It defines how individual users or applications see the data, providing customized views that match their needs while hiding the rest of the database. It focuses on what data is accessible and how it’s presented, without exposing how it’s stored or structured internally.

Purpose of the External Schema:

• To offer data abstraction and security by exposing only necessary data to users.

• To simplify interaction by customizing how data appears to each user or application.

• To support multiple views so that different departments or roles can access the same database in different ways.

Example: 

• A customer support rep sees only the CustomerName and OrderStatus, not the full order table.
• For example, in a university database:
• A student might see only their grades and personal info.
• A teacher might see the students in their class and their performance.
• An admin might access broader information like fees, courses, and student records.

Each of these views is part of the external schema, isolating users from the complexities of the full database.

Conceptual Schema (Logical Level).

The Conceptual Schema, also known as the Logical Level, is the middle layer in the Three-Schema Architecture. It defines the overall structure of the entire database for the organization, focusing on what data is stored and the relationships between data, without worrying about how it's physically stored.

This layer acts as a bridge between the external views (user perspective) and the internal storage (physical level). It ensures data consistency across all external views and maintains integrity and constraints.

Purpose of the Conceptual Schema:

• To provide a unified and abstract view of the entire database.
• To define relationships, data types, constraints, and business rules.
• To ensure consistency and isolation from physical storage changes.

Example: In a university database:

• The conceptual schema defines entities like Student, Course, Faculty, and Enrollment, along with their relationships.
• It knows a student can enroll in multiple courses and each course can have many students, but it doesn’t deal with indexes or how records are stored.

Internal Schema (Physical Level).

The Internal Schema, or Physical Level, is the lowest layer of the Three-Schema Architecture. It defines how the data is actually stored in the database—things like file structures, indexes, storage allocations, compression, and access methods.

This level is invisible to end users and even developers most of the time. Its main role is to optimize performance and manage storage efficiently.

Purpose of the Internal Schema:

• To manage data storage on physical devices like hard drives or SSDs.
• To optimize queries and operations through indexing and data organization.
• To handle low-level details like memory usage, file formats, and access paths.

Example: In the university database:

• The internal schema determines that student records are stored in a B-tree index for faster lookups.
• It stores data in binary format, grouped by pages, and allocates specific disk blocks.

Users and applications never directly interact with this layer, but it’s crucial for performance and reliability.

Note: Schema is a structural description of data. The schema doesn’t change frequently. Data may change frequently.

Benefits of Three-Schema Architecture.

• Data Independence: Physical storage changes do not affect user views.
• Security: Different users can have restricted access to sensitive data.
• Maintainability: Easier to manage and modify different aspects of the database without affecting others.
• Scalability: Supports large-scale database applications by managing complexity.

Why is Three Schema Architecture Important?

The Three-Schema Architecture plays a crucial role in separating different layers of a database system. It allows users and applications to interact only with the data they need, without worrying about how the data is stored or maintained. This separation ensures data independence, so any changes made to the storage structure or user views won’t disrupt the overall system.

In addition, this architecture greatly enhances security and access control. By defining different levels of schema, it ensures that users can only access the specific layer of data they are authorized to see. This protects sensitive information and keeps the database system more secure and organized.

Conclusion.

The Three-Schema Architecture in DBMS simplifies database management by separating concerns across three layers. It improves data security, scalability, and abstraction, making databases more robust and adaptable to change. Whether you're a developer, DBA, or student, understanding this model is key to designing efficient and secure database systems.

Frequently Asked Questions.

What are the three levels of the Three Schema Architecture?

• External Level (View Level): Custom views for users.
• Conceptual Level (Logical Level): Unified logical structure of the entire database.
• Internal Level (Storage Level): How data is physically stored.

How is Three Schema Architecture different from Three-Tier Architecture?

• Three-Schema Architecture is a logical framework that defines how data is viewed and stored at different abstraction levels (external, conceptual, internal), while Three-Tier Architecture is a physical system design that separates an application into presentation, application (business logic), and data tiers for scalability and maintainability.

Can multiple external schemas exist in the Three Schema Architecture?

• Yes, multiple external schemas can exist to support different user roles or applications accessing the same database differently.

DBMS Architecture: 1-Tier, 2-Tier, and 3-Tier Models.

A Database Management System (DBMS) acts like a smart manager between the user and the data stored in the system. Its architecture defines how different components of a DBMS interact with each other to store, retrieve, and manage data efficiently.

In simple terms, DBMS architecture is the blueprint that shows how a database system is designed, how it handles queries, stores data, ensures security, and provides backup. In this article, we’ll explore the different types of DBMS architectures (like 1-tier, 2-tier, and 3-tier) and why they matter in the world of modern applications.

What is DBMS Architecture?

When we hear the word architecture, we often think of buildings, how they’re planned, structured, and built to serve a purpose. In the world of databases, DBMS architecture is very similar. It refers to how different parts of a database system are structured and how they interact with each other to manage, store, and retrieve data.

DBMS Architecture is the design and structure that defines how users, applications, and databases talk to each other.

Just like a building has floors, rooms, and hallways, a DBMS has layers and components that perform different tasks, such as:

• Receiving requests from users

• Processing those requests

• Communicating with the database

• Returning the results back

Why is Architecture Important in DBMS?

A good architecture ensures that the system is:

• Efficient in Performance: A good DBMS architecture improves performance by separating tasks. In a 3-tier setup, the application server handles business logic, reducing the load on the database server and speeding up overall processing.

• Secure: Architecture defines clear access rules. In multi-tier architectures, sensitive operations are handled on backend servers that are not directly accessible to end users. This adds a layer of protection, reducing the risk of unauthorized access or data breaches.

• Scalable: Scalability is another major benefit of a well-planned DBMS architecture. In 3-tier systems, for example, it’s possible to scale horizontally by adding more application servers to manage increasing workloads, ensuring the system remains responsive and stable under load.

Types of DBMS Architecture.

There are several types of DBMS architecture available that we can use based on our requirements and needs. Let's discuss a few of them, which are more popularly used in real-life applications.

1-Tier Architecture (Single Tier).

1-Tier Architecture is the simplest form of DBMS architecture. In this setup, the database, the DBMS software, and the user interface all reside on the same machine. There is no client-server separation. Everything the user needs to access and manage the data is on one single layer.

Example: Let’s say you're learning SQL on your laptop using MySQL Workbench or SQL Server Management Studio (SSMS), where you've installed the DBMS software, created your own database, and run SQL queries directly on it. This is a 1-Tier Architecture, where everything happens on your own system.

1-Tier Architecture

Use a 1-Tier Architecture when you want to:

• Learn and Practice SQL.
• Build a Small tool for personal Use.
• Test Queries before deploying them to production.

Advantages of 1-Tier Architecture.

• 1-Tier Architecture is simple to set up and use, making it ideal for beginners and personal projects.
• It offers fast performance because all operations are executed locally without network delays.
• This architecture is great for development and testing, allowing developers to work directly on their own system.

Disadvantages of 1-Tier Architecture.

• 1-Tier Architecture is unsuitable for multi-user environments as it only supports one user at a time.
• It doesn’t allow remote access or real-time collaboration since everything runs on a single machine.
• It lacks scalability, making it inefficient for handling large datasets or growing user demands.

2-Tier Architecture (Client-Server).

2-Tier Architecture in DBMS is a client-server model where the application is split into two layers: the client (user interface) and the database (data storage). The client directly communicates with the database server to send queries and retrieve data. It is commonly used in small to medium-sized applications like desktop or intranet-based systems.

Example: In a retail store, a desktop inventory system installed on your computer acts as the client, directly connected to a central SQL Server that stores all data. When you search for an item, the app sends a query to the database, retrieves the result, and displays it instantly. This setup is a typical example of 2-Tier Architecture, where the client talks directly to the database.

2-Tier Architecture

Use a 2-Tier Architecture when building small to medium-sized applications where:

• Security and scalability are not major concerns.
• The number of users is limited.
• You need faster performance with direct database access.
• Ideal for LAN-based desktop apps like inventory or billing systems.

Advantages of 2-Tier Architecture.

• Easy to build and maintain for small-scale applications.
• Faster than multi-tier systems for simple transactions.
• Direct communication between the client and the database means less complexity.

Disadvantages of 2-Tier Architecture.

• Not ideal for large applications with complex business logic.
• Scalability is limited because all clients connect directly to the database.
• Security risks are higher since the database is exposed to the client layer.

3-Tier Architecture.

The 3-tier architecture in DBMS is a robust and scalable model that separates the application into three distinct layers: the presentation layer, application layer, and data layer. This structure allows developers to isolate user interface, business logic, and data storage concerns.

• The Presentation Layer is the user interface, like a browser or mobile app, where users interact with the application.
• The Application Layer contains business logic, often hosted on a server (e.g., .NET Core, Node.js), that processes data and handles rules.
• The Data Layer is the database server (e.g., SQL Server, MySQL) that stores and manages data.

Example: In an online shopping website, the user interface (presentation layer) runs in the browser, the server-side code that handles orders and payment (application layer) runs on a backend server, and the product data is stored in a database (data layer). When a user places an order, the request flows from the presentation layer to the application server, which applies business rules and then communicates with the database to fetch or update information.

3-Tier Architecture

Advantages of 3-Tier Architecture.

• The 3-tier architecture provides a clear separation of concerns, which improves code maintainability and simplifies application updates.
• This architecture enhances security by isolating the database from the client, reducing direct access risks.
• It improves scalability because additional servers can be added to handle application logic or user traffic without modifying the database or UI.
• Performance can be optimized because each layer can be tuned or scaled independently based on demand.
• Teams can simultaneously work on different layers (UI, business logic, database), speeding up development time.

Disadvantages of 3-Tier Architecture.

• 3-tier systems are more complex to develop and require careful coordination between layers.
• Deploying and managing separate layers may increase infrastructure and operational costs.
• Debugging and troubleshooting can be slower since issues may span across multiple layers.
• Network latency may increase slightly due to the communication between layers.

In conclusion, DBMS architecture plays a crucial role in how database systems are structured, accessed, and maintained. Whether it's the simplicity of 1-tier, the directness of 2-tier, or the scalability of 3-tier, each architecture serves specific use cases based on application size, performance, and security needs. Understanding these models helps developers choose the right architecture for building efficient and secure data-driven systems.

Next Read: Three Schema Architecture in DBMS.

Difference Between Data and Information.

The terms "data" and "information" are often used interchangeably, but they represent distinct concepts with unique characteristics and roles. Clearing the confusion between these two is crucial for effective data management and decision-making. Let's understand the difference between them with some real examples.

What is Data?

At its core, data refers to raw and unprocessed facts, figures, or symbols. It constitutes the basic elements that, on their own, lack context, meaning, or relevance. Data can take various forms, including numbers, text, or symbols, and it serves as the foundation for information. Think of data as the individual pieces of a puzzle – isolated and meaningless without proper arrangement and interpretation.

Characteristics of Data:

• Data is objective and neutral, presenting facts without interpretation.
• Data can be either quantitative (numeric) or qualitative (non-numeric).
• Data is unprocessed and lacks organization or structure.
• Data, in its raw form, has limited usefulness until processed and interpreted.

What is Information?

In contrast, information is the result of processing and organizing data to provide context, meaning, and relevance. It represents a higher level of abstraction, where data is refined, interpreted, and transformed into a usable and meaningful form. Information is what emerges when data is put into a context that facilitates understanding, analysis, and decision-making.

Characteristics of Information:

• Information is subjective and depends on the interpretation of the observer.
• Information is presented in a structured manner, adding context to the data.
• Information has meaning and relevance, allowing it to be used for specific purposes.
• Information is designed to support decision-making, problem-solving, or communication.

Data to Information.

The transformation from data to information involves a series of steps, including collection, organization, analysis, and interpretation. Consider a set of temperature readings (data) over a week. By organizing this data into a weekly weather report with trends, highs, lows, and contextual information, it becomes meaningful information for someone planning outdoor activities.

Key Difference Between Data and Information.

Data	Information
Data is Raw and unprocessed facts or symbols.	Information is Processed and organized data with meaning.
Data is Objective; presents facts without interpretation.	Information is Subjective; and depends on the interpretation of the observer.
Data Can be numeric, text, or symbols.	Information is Presented in a structured manner.
Data Lacks context; individual pieces of a puzzle.	Information Provides context and relevance.
Data is Often presented as individual elements.	Presented in a structured and organized manner.
Example: Numbers, text, symbols.	Example: Reports, charts, summaries, analysis.

Data becomes valuable when transformed into information, which is used for decision-making, gaining insights, and communicating meaningful findings.

In the digital age, where vast amounts of data are generated daily, understanding the distinction between data and information is pivotal. Organizations and individuals alike benefit from harnessing the power of both collecting and managing data effectively and transforming it into actionable information for informed decision-making.

DBMS Introduction.

What is Data?

Data refers to raw facts, figures, or information that can be recorded, stored, and processed. It is the basic building block of knowledge and is often the result of observations, measurements, or representations of real-world phenomena. Data can take various forms, including numbers, text, images, audio, and more.

Types of Data.

There are two primary types of data:

1. Quantitative Data:

Quantitative data represents measurable quantities and is expressed in numerical terms. This type of data is inherently numerical and can be subjected to mathematical operations, making it suitable for statistical analysis. 

Quantitative data can be further categorized into two subtypes:

• Discrete Data: Discrete data consists of separate, distinct values with no possible values in between. These values are typically counted in whole numbers. Examples include the number of students in a class, the number of cars in a parking lot, or the number of books on a shelf.
• Continuous Data: Continuous data, on the other hand, can take any value within a given range. It is often measured with greater precision and can include decimal values. Examples include temperature measurements, weight, height, or distance.

2. Qualitative Data: 

Qualitative data represents non-numeric information and is descriptive in nature. This type of data provides insights into qualities, characteristics, or attributes and is often used to capture subjective information. 

Qualitative data can be further categorized into two subtypes:

• Nominal Data: Nominal data represents categories with no inherent order or ranking. It is used to label variables without assigning any quantitative value. Examples include colors, gender, or types of fruits.
• Ordinal Data: Ordinal data represents categories with a meaningful order or ranking. While the differences between categories are not precisely measured, there is a clear sequence. Examples include educational levels (e.g., high school, bachelor's, master's) or customer satisfaction ratings (e.g., low, medium, high).

What is Information?

Information refers to processed and organized data that has meaning and relevance. It is the result of analyzing, interpreting, and contextualizing raw data to extract useful insights or knowledge. Information provides a meaningful understanding of a subject or situation and is used to support decision-making, problem-solving, or communication.

In essence, information adds value to data by giving it context and making it useful. For example, a list of numbers (data) becomes information when it is organized into a statistical chart, allowing viewers to understand trends or patterns.

Information can take various forms, including textual, visual, or auditory representations. It is communicated through reports, charts, graphs, articles, or any medium that conveys a message derived from data analysis.

Data Vs Information.

While data and information are related concepts, they have distinct characteristics. Data is the raw and unprocessed facts or figures, whereas information is the result of processing and interpreting that data to provide meaning. Data is often considered the input, while information is the output of the data processing cycle.

Data lacks context and may not necessarily convey meaningful insights on its own. It becomes information when it is organized, analyzed, and presented in a way that makes it useful and understandable. Information is more refined, actionable, and geared towards facilitating decision-making or understanding a specific context.

Data	Information
Data refers to raw and unprocessed facts, figures, or symbols.	Information is processed and organized data that provides context, meaning, and relevance.
Data is often in the form of numbers, text, or symbols and lacks context or meaning on its own.	Information is the result of analyzing and interpreting data, making it useful and understandable.
Data is objective, neutral, and can be quantitative or qualitative.	Information is subjective, contextual, and often presented in a structured format.
Data is the input for information and requires processing to become meaningful.	Information is used for decision-making, communication, and gaining insights.
A series of numbers (e.g., 12345) or a list of names (e.g., John, Mary, Jane) is considered data.	A graph showing the sales trends over the past year or a report summarizing customer feedback is considered information.

What is a Database?

A database is a structured and organized collection of data that is stored and managed systematically to enable efficient retrieval, update, and management of information. It acts as a central repository for storing data in a way that facilitates easy access and manipulation. Databases are crucial components in various applications and systems, providing a structured method for storing and organizing data.

A database consists of tables, where each table represents a specific entity and columns within the table represent attributes or fields of that entity. Relationships between tables are established to represent connections or associations between different entities. This structured approach helps maintain data integrity and ensures efficient querying of information.

Example:

Consider a library management system. In this scenario, the database could include tables for 'Books,' 'Authors,' and 'Customers.' The 'Books' table may have columns such as 'Title,' 'ISBN,' and 'Availability.' The 'Authors' table might include information like 'AuthorID' and 'AuthorName.' Relationships between these tables can link books to authors, creating a comprehensive and organized system for managing library data. The database structure facilitates the easy retrieval of information, such as finding available books by a specific author or tracking customer borrowing history.

What is DBMS?

A Database Management System (DBMS) is a software application or system that provides an interface for managing and interacting with databases. Its primary function is to enable users to efficiently store, retrieve, update, and manage data in a structured and organized manner. DBMS acts as an intermediary between the database and the end-users or applications, facilitating seamless and secure interaction with the stored information.

Key features of a DBMS include:

• Data Definition: DBMS allows users to define the structure of the database, including tables, relationships, and constraints, using a Data Definition Language (DDL).
• Data Manipulation: It provides tools for inserting, updating, and deleting data using a Data Manipulation Language (DML), often through query languages like SQL (Structured Query Language).
• Data Retrieval: DBMS allows users to retrieve and query data based on specific criteria, using SELECT statements or other query mechanisms.
• Concurrency Control: DBMS manages concurrent access to the database, ensuring data consistency and preventing conflicts when multiple users or applications attempt to modify the same data simultaneously.
• Data Integrity: DBMS enforces data integrity by applying constraints, such as primary keys, foreign keys, and unique constraints, to maintain accuracy and reliability in the stored data.
• Security: It implements security measures, including user authentication, access control, and encryption, to protect sensitive data and ensure that only authorized users can perform specific actions.
• Transaction Management: DBMS supports transactions, allowing users to group multiple database operations into a single unit of work. Transactions follow the principles of ACID (Atomicity, Consistency, Isolation, Durability).
• Backup and Recovery: DBMS provides mechanisms for backing up data regularly and recovering data in case of system failures, ensuring data availability and reliability.

Example:

Popular examples of DBMS include MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database, and SQLite. These systems are used across various industries and applications to manage and organize vast amounts of data efficiently.

DBMS Vs File System.

File System is an old way of storing and manipulating data and DBMS is a modern system so we need to understand the basic difference between them and why DBMS is a better option.

File System	DBMS
In a file system, data is stored in files, often with a hierarchical directory structure.	In a DBMS, data is stored in a centralized database, providing a more organized and efficient way to manage data.
Retrieving data from a file system requires custom code within each application.	DBMS allows users to retrieve and query data using a standardized language like SQL.
Changes to the data structure or format often require modifications to all applications that use the data, leading to a lack of data independence.	DBMS provides both logical and physical data independence. Changes to the data structure do not affect the application, promoting easier maintenance and adaptation.
Ensuring data integrity is the responsibility of individual applications in the File System.	DBMS enforces data integrity through constraints, ensuring that data remains accurate and consistent.
File systems lack built-in mechanisms for managing concurrent access to data, making it challenging to maintain consistency in multi-user environments.	DBMS supports mechanisms for managing concurrent access to data, ensuring consistency, and preventing conflicts between multiple users.

Advantages of Database Management Systems (DBMS).

• Data Centralization: DBMS centralizes data storage, providing a single, unified repository for efficient data management.
• Data Sharing: DBMS allows multiple users and applications to access and share data concurrently, promoting collaboration and reducing data redundancy.
• Data Integrity: DBMS enforces data integrity through constraints, ensuring accuracy and consistency in the stored information.
• Data Independence: DBMS provides both logical and physical data independence, allowing changes to the data structure without affecting applications that use the data.
• Efficient Data Retrieval: With a standardized query language like SQL, DBMS facilitates efficient and flexible data retrieval, enabling complex queries and reports.
• Concurrency Control: DBMS manages concurrent access to data, ensuring consistency and preventing conflicts when multiple users or applications modify the same data simultaneously.
• Security Measures: DBMS offers robust security features, including user authentication, access control, and encryption, to protect sensitive data from unauthorized access.
• Scalability: DBMS systems are scalable, allowing for the efficient handling of large datasets and adapting to growing storage and processing needs.
• Backup and Recovery: DBMS includes mechanisms for regular data backup and recovery, minimizing the risk of data loss in case of system failures.
• Reduced Data Redundancy: By organizing data in a structured manner, DBMS reduces data redundancy, eliminating unnecessary duplication of information.
• Data Consistency: DBMS maintains data consistency by ensuring that changes made to the data are accurate and reflect the intended modifications across the entire database.
• Data Organization: DBMS organizes data in a structured way, improving overall data organization and making it easier to manage and understand.
• Query Optimization: DBMS includes optimization techniques to enhance the performance of queries, ensuring efficient data retrieval and processing.
• Enhanced Data Security: With centralized control over security measures, DBMS provides a more secure environment for sensitive data, reducing the risk of unauthorized access.

These advantages make DBMS a fundamental component in various industries and applications, providing efficient and secure means of managing and leveraging vast amounts of data.

HTTP Status Code: 100 Continue

The HTTP status code 100 Continue is part of the 1xx series, which represents informational responses. Specifically, 100 Continue is used to indicate that the initial part of the client's request has been received by the server, and the server is prompting the client to proceed with sending the remainder of the request. This status code is typically utilized in scenarios involving large payloads or in situations where a server wants to confirm that the client can continue before processing the entire request.

Conditions for Receiving 100 Continue.

The HTTP status code 100 Continue is received under specific conditions when a client sends a request with an Expect: 100-continue header. Here are the conditions for receiving the 100 Continue status:

• Expect Header Present: The client includes the Expect: 100-continue header in its initial request. This header serves as a signal to the server that the client expects to receive a 100 Continue response before proceeding to send the full request payload.
• Server Readiness: The server is ready and able to receive the remaining part of the client's request. The server may use this status code to communicate to the client that it can proceed with sending the entire request payload.
• Client's Willingness to Wait: The client is willing to wait for the server's confirmation before sending the full payload. By including the Expect: 100-continue header, the client indicates its intention to wait for the server's acknowledgment before proceeding with the request.

Example: 

Client Request.

POST /upload HTTP/1.1
Host: example.com
Content-Length: 10000
Expect: 100-continue

<... additional headers and payload ...>

Server Response (100 Continue).

HTTP/1.1 100 Continue

In this example, the conditions are met as the client includes the Expect: 100-continue header, signaling its willingness to wait for acknowledgment, and the server responds with a 100 Continue status, indicating readiness to receive the remaining payload.

Use Cases of 100 Continue:

• Large File Uploads: When a client is uploading a large file, it may include the Expect: 100-continue header to ensure the server is ready before sending the entire file.
• Resource-Intensive Requests: In situations where the server needs to perform resource-intensive processing, it can use 100 Continue to signal the client to proceed only if the server is prepared to handle the request.

How To Handle 100 Continue Status Code?

Handling the HTTP status code 100 Continue involves specific actions on both the client and server sides. This status code is utilized in scenarios where the client includes the Expect: 100-continue header in the request, indicating its intention to wait for acknowledgment from the server before sending the full payload. Here's how to handle the 100 Continue status:

• Client's Responsibility: The client should include the Expect: 100-continue header in the initial request if it is willing to wait for the server's confirmation before sending the full payload.
• Wait for Confirmation: Upon receiving a 100 Continue response, the client can proceed to send the rest of the request.
• Handling Delays: If the client doesn't receive a 100 Continue response, it may choose to wait for a reasonable amount of time before deciding whether to proceed or take appropriate action.

Best Practice.

• Use the 100 Continue mechanism in scenarios where it can improve the efficiency of data transmission, especially for large payloads.
• Implement mechanisms to handle situations where the client does not receive a 100 Continue response within a reasonable timeframe.
• Clients may choose to wait for a specific duration and then decide whether to proceed without acknowledgment.
• Both clients and servers should gracefully handle situations where a 100 Continue response is expected but not received, allowing for sensible fallback mechanisms.

Conclusion.

HTTP status code 100 Continue facilitates more efficient communication between clients and servers, especially in scenarios involving substantial data transfers. Clients using the Expect: 100-continue header can ensure that the server is ready to receive the full request payload before committing to the transmission. This status code contributes to the overall optimization of data exchange in the HTTP protocol.

Also Read: HTTP Status Code: 101 Switching Protocols.

Introudction to SQL.

In this article, we cover the basic introduction of SQL and why it is important to learn this query language. 

What is SQL?

SQL, or Structured Query Language, is a domain-specific language for managing and manipulating relational databases. It provides a standardized way to interact with databases, enabling users to define, query, and manipulate data within a relational database management system (RDBMS). SQL is not a programming language but a declarative language used to express database operations.

Why do we need SQL?

SQL is the cornerstone of database management, enabling users to interact with and manipulate data efficiently. SQL plays a pivotal role in modern data-driven environments, from storing information to retrieving insights. Here is a list of some important situation in which SQL play an important role.

• Data Retrieval: SQL is essential for retrieving specific data from databases. The SELECT statement allows users to query databases and fetch the required information.
• Data Modification: SQL provides commands like INSERT, UPDATE, and DELETE, enabling users to add new records, modify existing ones, or remove data from a database.
• Database Creation and Modification: With SQL, users can create databases, define tables, set relationships between tables, and modify the structure of existing databases using commands like CREATE, ALTER, and DROP.
• Data Security: SQL includes features for managing user access and permissions, allowing administrators to control who can perform various operations on the database.
• Data Indexing: SQL allows the creation of indexes on tables, enhancing query performance by speeding up data retrieval operations.
• Compatibility and Standardization: SQL is a standardized language, that ensures consistency across different database systems. This compatibility allows users to switch between different database vendors with relative ease.
• Integration with Programming Languages: SQL is often integrated with programming languages like Java, Python, and others, allowing seamless interaction between databases and application code.

History of SQL.

SQL, or Structured Query Language, was developed in the early 1970s by researchers at IBM led by Donald D. Chamberlin and Raymond F. Boyce. Initially called SEQUEL (Structured English QUEry Language), it aimed to provide a standardized and user-friendly way to interact with databases. The first formalized version, SQL-86, was adopted as an industry standard by the American National Standards Institute (ANSI) in 1986. Since then, SQL has undergone several revisions, with SQL-92, SQL:1999, SQL:2003, and subsequent versions adding new features and capabilities. 

SQL has become the de facto language for managing relational databases, and its standardized nature has allowed for widespread adoption across various database management systems, including MySQL, PostgreSQL, Microsoft SQL Server, Oracle, and SQLite. Today, SQL is a fundamental tool in the field of data management and is used globally for tasks such as querying, updating, and managing relational databases.

Components of SQL System.

The components of SQL (Structured Query Language) can be broadly categorized into several key aspects:

Data Definition Language (DDL).

SQL's DDL component comprises commands that allow users to define and manage the structure of the database. The CREATE statement is used to create various database objects, such as tables, indexes, and views. With ALTER, users can modify existing structures, while DROP deletes database objects when necessary.

Data Manipulation Language (DML).

DML commands empower users to interact with the data stored in the database. The foundational SELECT statement retrieves data from one or more tables, while INSERT, UPDATE, and DELETE facilitate the addition, modification, and deletion of records in tables.

Data Control Language (DCL).

Security is paramount in any database system. DCL commands, such as GRANT and REVOKE, provide the means to assign specific privileges to users, controlling access to various database objects and operations.

Transaction Control Language (TCL).

TCL commands manage the transactional aspects of the database. COMMIT finalizes changes made during a transaction, while ROLLBACK undoes changes. SAVEPOINT allows users to mark points within a transaction for later rollback.

Data Query Language (DQL).

DQL, primarily represented by the SELECT statement, is focused on extracting data from one or more tables. It plays a central role in retrieving information based on specific criteria, sorting, and aggregating data.

MySQL Vs NoSQL.

MySQL: MySQL is a widely used relational database management system (RDBMS) that follows the principles of a traditional relational database. It uses a structured query language (SQL) for defining and manipulating data. MySQL is known for its reliability, stability, and ACID compliance, making it suitable for applications where data integrity and consistency are crucial. It supports a tabular data structure, with tables consisting of rows and columns, and employs a predefined schema. MySQL is an open-source database, making it accessible to a broad community of users and developers.

NoSQL: NoSQL, on the other hand, represents a category of databases that do not adhere strictly to the traditional relational model. NoSQL databases are designed to handle unstructured, semi-structured, or structured data, providing more flexibility for applications with evolving data requirements. Unlike MySQL, NoSQL databases often do not require a fixed schema, allowing for dynamic and scalable data storage. NoSQL databases are particularly well-suited for handling large amounts of distributed data and are known for their horizontal scalability. They come in various types, including document-oriented, key-value stores, column-family stores, and graph databases.

MySQL databases are vertically scalable, meaning that to handle increased load, you typically need to add more resources to a single server. NoSQL databases, in contrast, are often horizontally scalable, enabling them to distribute data across multiple servers, and providing a more efficient solution for handling increased workloads.

MySQL requires a predefined schema where the structure of the data (tables, columns, and relationships) needs to be defined before data insertion. NoSQL databases, being schema-less or schema-flexible, allow for the dynamic addition of fields without a predefined structure, making them more adaptable to changing data models.