#1 – What is database?

A database is a collection of data that is organized in a way that allows for efficient storage and retrieval. Databases are used to store and manage data in many different applications, from small personal databases to large enterprise-level systems. They are a critical component of many modern computer systems, and are essential for efficient data management and analysis.

#2 – History of Database

The history of databases dates back to the 1960s, when the first modern computer databases were developed. These early databases were designed to support business operations, and were based on the concept of a hierarchical data model, which organized data into a tree-like structure with a parent-child relationship between the data elements.

Over time, the field of database technology has evolved, and new database models and designs have been developed. For example, the relational model, which was introduced in the 1970s, allowed data to be organized in tables with rows and columns, and allowed for the creation of complex relationships between different data elements. This made it possible to store and manage large amounts of data more efficiently, and paved the way for the development of modern database management systems (DBMS).

Today, databases are an essential part of many different applications, and are used to store and manage data in a wide range of fields, including business, science, and government. The widespread adoption of the internet and the growth of big data have also led to the development of new database technologies, such as NoSQL databases and cloud-based databases, which are designed to handle large amounts of data more efficiently.

#3 – Database Terminologies

Here are some common terms that are used in the field of databases:

• Database: A collection of data that is organized in a specific way, allowing for efficient storage and retrieval.
• Database management system (DBMS): A software program that is used to create, manage, and interact with a database.
• Data model: The structure or organization of data in a database, which defines how data is stored and accessed.
• Table: A collection of data that is organized into rows and columns, similar to a spreadsheet. Tables are a common way to represent data in a relational database.
• Field: A single piece of data within a table, such as a customer’s name or email address.
• Record: A collection of fields that represent a single entity, such as a customer or a product.
• Query: A request to retrieve specific data from a database.
• Index: A data structure that allows for efficient search and retrieval of data within a database.
• Primary key: A field or set of fields that uniquely identifies each record in a table.
• Foreign key: A field or set of fields that refers to the primary key of another table, allowing for the creation of relationships between tables.
• Normalization: The process of organizing data in a database in a way that minimizes redundancy and dependency, and maximizes data integrity.
• SQL: Structured Query Language, a standard language for interacting with databases.
• NoSQL: A class of databases that do not use the traditional relational model, and are designed to handle large amounts of data more efficiently.
• Cloud database: A database that is hosted on a cloud computing platform, allowing for easy scalability and access from anywhere.

#4 – Components of Database

There are several key components of a database, which work together to support the storage and management of data. These components include:

4.1 – Database Software

This is the main program that is used to create, manage, and interact with the database. It includes tools for defining the structure of the database, importing and exporting data, and executing queries and other operations on the data. Examples of database software include MySQL, Oracle, and Microsoft SQL Server.

4.2 – Data Storage

This is the physical location where the data is stored, such as on a hard drive or solid-state drive. The data storage component of a database is typically managed by the database software, and may use a variety of different technologies and formats to store the data.

4.3 – Data Model

This is the structure or organization of data in the database, which defines how the data is stored and accessed. Different data models have different strengths and weaknesses, and are designed to support different types of applications and workloads. Examples of data models include the hierarchical model, the relational model, and the object-oriented model.

4.4 – User Interface

This is the part of the database that allows users to interact with the data, such as through a graphical user interface (GUI) or a command-line interface (CLI). The user interface may provide tools for querying the database, viewing and editing data, and creating reports and other outputs.

4.5 – Query Language

This is the language or syntax that is used to formulate requests for data from the database. Different databases may use different query languages, but many databases support Structured Query Language (SQL), which is a standard language for accessing and manipulating data in relational databases.

4.6 – Indexes

These are data structures that are used to speed up the search and retrieval of data within the database. Indexes are created on specific fields or sets of fields in a table, and allow the database software to quickly locate the records that match a particular search criteria.

4.7 – Security

This is the set of rules and controls that are put in place to protect the data in the database, and to ensure that only authorized users can access the data. Security measures may include authentication, access control, and encryption, and are designed to prevent unauthorized access and data leaks.

#5 – Types of Database

There are several different types of databases, which are designed to support different types of applications and workloads. Some common types of databases include:

5.1 – Relational Databases

These are the most common type of database, and are based on the relational model, which organizes data into tables with rows and columns. Relational databases are well-suited for applications that need to store and manipulate structured data, and support complex queries and data relationships. Examples of relational databases include MySQL, Oracle, and Microsoft SQL Server.

5.2 – NoSQL Databases

These are a class of databases that do not use the traditional relational model, and are designed to handle large amounts of data more efficiently. NoSQL databases are often used in applications that need to store and process unstructured or semi-structured data, such as social media posts or sensor data. Examples of NoSQL databases include MongoDB, Cassandra, and CouchDB.

5.3 – In-memory Databases

These are databases that store data in the main memory (RAM) of a computer, rather than on a disk or other persistent storage. In-memory databases are often used for real-time applications that need to access data quickly, such as online transaction processing (OLTP) systems. Examples of in-memory databases include SAP HANA and Apache Ignite.

5.4 – Distributed Databases

These are databases that are spread across multiple machines or nodes, and are designed to support high availability and scalability. Distributed databases are often used in applications that need to process large amounts of data in parallel, such as data warehouses and big data analytics platforms. Examples of distributed databases include Apache Hadoop and Google Cloud Bigtable.

5.5 – Cloud Databases

These are databases that are hosted on a cloud computing platform, such as Amazon Web Services (AWS) or Microsoft Azure. Cloud databases provide many benefits, such as easy scalability, high availability, and reduced maintenance overhead. Examples of cloud databases include Amazon Aurora and Microsoft Azure SQL Database.

5.6 – Graph Databases

These are databases that are designed to store and query data that is represented as a graph, with nodes, edges, and properties. Graph databases are often used in applications that need to store and process complex, interconnected data, such as social networks or supply chain networks. Examples of graph databases include Neo4j and Apache TinkerPop.

#6 – Role of Database

The role of a database is to store and manage data in a way that allows for efficient access and retrieval. Databases are used in many different applications, and play a critical role in the operation of modern computer systems. Some common roles of databases include:

1. Storing and organizing data: Databases provide a structured way to store data, allowing it to be organized and accessed in a consistent and predictable manner. This makes it possible to store and manage large amounts of data more efficiently, and to retrieve specific data quickly and accurately.
2. Enforcing data integrity: Databases include tools and mechanisms for ensuring the integrity of the data, such as constraints, triggers, and transactions. This helps to prevent data corruption and inconsistencies, and ensures that the data remains accurate and reliable.
3. Supporting data relationships: Databases allow for the creation of relationships between different data elements, allowing for the storage of complex, interconnected data. This makes it possible to represent real-world entities and their relationships, and to query and manipulate the data in meaningful ways.
4. Providing data security: Databases include security features that help to protect the data from unauthorized access, tampering, and loss. This ensures that the data remains confidential and available only to authorized users, and helps to prevent data breaches and other security threats.
5. Enabling data analysis: Databases provide tools and mechanisms for analyzing data, such as SQL query language and indexing. This makes it possible to extract insights and knowledge from the data, and to support data-driven decision making and business processes.
6. Facilitating data sharing: Databases support the sharing of data between different applications and users, allowing for the creation of data-driven systems and ecosystems. This enables collaboration, integration, and interoperability, and helps to drive innovation and productivity.

#7 – Database Functions

A database performs several key functions to support the storage and management of data. Some of the key functions of a database include:

7.1 – Storing Data

The primary function of a database is to store data in a structured and organized manner. This involves creating tables, fields, and records to represent the data, and defining the relationships between different data elements.

7.2 – Indexing Data

A database may create indexes on specific fields or sets of fields in a table, in order to support efficient search and retrieval of data. Indexes allow the database to quickly locate records that match a particular search criteria, and can significantly improve the performance of queries and other operations on the data.

7.3 – Enforcing Constraints

A database may include constraints, which are rules that are used to enforce the integrity of the data. Constraints may be defined at the field level (e.g. a field must not be null) or at the table level (e.g. a record must have a unique primary key), and help to prevent data corruption and inconsistencies.

7.4 – Executing Queries

A database provides tools and mechanisms for executing queries, which are requests to retrieve specific data from the database. Queries may be written in a query language, such as SQL, and may include complex conditions and operations, such as joins and aggregations.

7.5 – Transacting Data

A database supports the use of transactions, which are units of work that are executed atomically, meaning they either succeed or fail as a whole. Transactions help to ensure the consistency and integrity of the data, by allowing multiple operations to be executed together and rolled back if necessary.

7.6 – Providing Security

A database includes security features that are used to protect the data from unauthorized access and tampering. These may include authentication, access control, and encryption, and help to prevent data breaches and other security threats.

7.7 – Backing up data

A database may include tools and mechanisms for backing up data, which involves creating copies of the data and storing them in a safe and secure location. This is important in case of data loss or corruption, and allows the database to be restored to a previous state if necessary.

7.8 – Monitoring and Optimizing Performance

A database may include tools for monitoring and optimizing its performance, such as logs, metrics, and performance counters. This allows the database administrator to identify and troubleshoot any performance issues, and to make adjustments to improve the efficiency and reliability of the database.

#8 – Database or DBMS

Database and DBMS (database management system) are related but distinct concepts. A database is a collection of data that is organized in a specific way, allowing for efficient storage and retrieval. A DBMS, on the other hand, is a software program that is used to create, manage, and interact with a database. In other words, a database is the data itself, while a DBMS is the software that is used to manage the data.

A DBMS provides a variety of functions and features that are designed to support the creation and management of databases. For example, a DBMS may include tools for defining the structure of the database, importing and exporting data, and executing queries and other operations on the data. It may also include features for enforcing data integrity, supporting data relationships, and providing security and backup.

A database is the collection of data that is being managed, while a DBMS is the software that is used to manage the data. The two concepts are closely related, and are typically used together to support the storage and management of data in modern computer systems.

#9 – DBMS

A database management system (DBMS) is a software program that is used to create, manage, and interact with a database. A DBMS provides a variety of functions and features that are designed to support the creation and management of databases, and is an essential component of many modern computer systems.

Some common features and functions of a DBMS include:

• Creating and defining the structure of a database: A DBMS provides tools for defining the structure of a database, including the tables, fields, and relationships between different data elements. This allows the DBMS to store the data in a structured and organized manner, and to enforce data integrity and consistency.
• Importing and exporting data: A DBMS provides mechanisms for importing data from external sources, such as files or other databases, and for exporting data to other applications or formats. This allows for the easy transfer of data between different systems and environments.
• Executing queries: A DBMS includes a query language, such as SQL, that allows users to formulate requests for data from the database. The DBMS parses and executes the query, and returns the results in a structured format.
• Enforcing constraints and rules: A DBMS may include constraints, which are rules that are used to enforce the integrity of the data. Constraints may be defined at the field level (e.g. a field must not be null) or at the table level (e.g. a record must have a unique primary key), and help to prevent data corruption and inconsistencies.
• Supporting transactions: A DBMS supports the use of transactions, which are units of work that are executed atomically, meaning they either succeed or fail as a whole. Transactions help to ensure the consistency and integrity of the data, by allowing multiple operations to be executed together and rolled back if necessary.
• Providing security: A DBMS includes security features that are used to protect the data from unauthorized access and tampering. These may include authentication, access control, and encryption, and help to prevent data breaches and other security threats.
• Monitoring and optimizing performance: A DBMS may include tools for monitoring and optimizing its performance, such as logs, metrics, and performance counters. This allows the database administrator to identify and troubleshoot any performance issues, and to make adjustments to improve the efficiency and reliability of the database.
• Backing up data: A DBMS may include tools and mechanisms for backing up data, which involves creating copies of the data and storing them in a safe and secure location. This is important in case of data loss or corruption, and allows the database to be restored to a previous state if necessary.

#10 – Database Languages

There are several different languages that are used to interact with databases, depending on the type of database and the specific application. Some common database languages include:

10.1 – Structured Query Language (SQL)

SQL is a standard language for accessing and manipulating data in relational databases. It is used to define the structure of a database, to import and export data, and to execute queries and other operations on the data. SQL supports a wide range of operations, including data definition, data manipulation, and data control.

10.2 – NoSQL Query Languages

NoSQL databases, which are a class of databases that do not use the traditional relational model, often use their own proprietary query languages. These languages may be similar to SQL, but may also include unique features and syntax that are specific to the NoSQL database. Examples of NoSQL query languages include MongoDB Query Language (MQL) and Cassandra Query Language (CQL).

10.3 – Procedural Languages

Some databases support procedural languages, which are programming languages that are used to write code that can be executed by the database. These languages may be used to create stored procedures, which are pre-defined units of code that can be executed by the database, or to write custom functions and triggers that can be used to manipulate and control the data in the database. Examples of procedural languages include PL/SQL, T-SQL, and Transact-SQL.

10.4 – Data Definition Languages (DDL)

DDL is a type of language that is used to define the structure and schema of a database. It is typically used to create tables, fields, and other database objects, and to specify the relationships and constraints that are applied to the data. Examples of DDL include CREATE, ALTER, and DROP.

10.5 – Data Manipulation Languages (DML)

DML is a type of language that is used to manipulate the data in a database. It is used to insert, update, and delete data in the database, and to query the data in order to retrieve specific records or aggregate information. Examples of DML include SELECT, INSERT, and UPDATE.

10.6 – Data Control Languages (DCL)

DCL is a type of language that is used to control access to the data in a database. It is used to grant and revoke access to the database, and to manage user accounts and permissions. Examples of DCL include GRANT and REVOKE.

#11 – Database Design and Models

Database design and database modeling are two related but distinct concepts that are used in the development of a database. Database design is the process of planning and defining the structure of a database, including the tables, fields, relationships, and constraints that are applied to the data. Database modeling, on the other hand, is the process of creating a visual representation of the database, using a diagram or other graphical notation.

Database design is an important step in the development of a database, as it defines the structure and organization of the data, and specifies how the data will be stored and accessed. Good database design can help to ensure the efficiency, reliability, and scalability of the database, and can make it easier to maintain and modify the database over time.

Database modeling is often used as a tool to support the design process, by providing a visual representation of the database that can be used to communicate and collaborate with other stakeholders. Database models can be created using diagramming tools, such as the Entity-Relationship (ER) model or the Unified Modeling Language (UML). These diagrams can be used to represent the tables, fields, and relationships in the database, and to document the design decisions and constraints that are applied to the data.

#12 – Database Advantages

Databases have several key advantages that make them an essential component of many modern computer systems. Some of the main advantages of databases include:

12.1 – Efficient storage and retrieval of data

Databases provide a structured and organized way to store data, allowing for efficient access and retrieval. This makes it possible to store and manage large amounts of data more effectively, and to retrieve specific data quickly and accurately.

12.2 – Enforcing data integrity and consistency

Databases include tools and mechanisms for ensuring the integrity and consistency of the data, such as constraints and transactions. This helps to prevent data corruption and inconsistencies, and ensures that the data remains accurate and reliable.

12.3 – Supporting complex data relationships

Databases allow for the creation of relationships between different data elements, allowing for the storage of complex, interconnected data. This makes it possible to represent real-world entities and their relationships, and to query and manipulate the data in meaningful ways.

12.4 – Providing data security

Databases include security features that are used to protect the data from unauthorized access and tampering. This ensures that the data remains confidential and available only to authorized users, and helps to prevent data breaches and other security threats.

12.5 – Facilitating data sharing and integration

Databases support the sharing of data between different applications and users, allowing for the creation of data-driven systems and ecosystems. This enables collaboration, integration, and interoperability, and helps to drive innovation and productivity.

12.6 – Enabling data analysis and decision making

Databases provide tools and mechanisms for analyzing data, such as SQL query language and indexing. This makes it possible to extract insights and knowledge from the data, and to support data-driven decision making and business processes.

12.7 – Offering scalability and flexibility

Databases can be easily scaled up or down, depending on the needs of the application. This allows for the efficient management of data, even as the amount of data grows or the requirements

More to Read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• Data Warehouse vs Database
• Dataset vs Database
• Database vs DataFrame
• Database Metadata
• 6 Types of Keys in Database
• Postgres Schema vs Database
• Relational Database vs Flat File
• Primary Key vs Foreign Key
• Primary Key vs Candidate Key
• Document Database Vs. Key Value Store
• Document Database Vs. Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Graph Databases
• Document Database
• Centralized Database
• Multidimensional Database

Metadata is used in a database to provide additional information about the data stored in the database. This information can include details about the structure of the data, such as the names and data types of fields, as well as information about the data itself, such as its source, quality, and lineage.

What is Database Metadata?

Database metadata refers to data that describes the structure of a database, including information about the tables, columns, indexes, constraints, and other elements that make up the database. This metadata can be used to understand the organization and relationships of the data in the database, as well as to manage and maintain the database.

Database metadata can be used by database management systems (DBMS) as well as by developers, data analysts, and other users to understand the schema of a database and its components. Database metadata can be accessed and queried through a variety of methods such as SQL, specialized APIs and system catalog views.

Some examples of information that might be included in database metadata include:

• The names, data types, and constraints for columns in a table.
• The relationships between tables, such as foreign key constraints.
• The indexes and keys that have been defined for the tables.
• The names and locations of the physical files that make up the database.
• The permissions and access controls that have been set up for the database.
• The history of changes made to the database, such as when tables or columns were added or modified.

The term “metadata” can refer to data about data in general, not just in the context of databases.

Why Metadata is Used?

There are several reasons why metadata is used in a database:

1. Data management: Metadata can be used to help manage the data stored in a database by providing information about the structure of the data and the relationships between tables. This can make it easier to create and maintain the database, as well as to understand and query the data.
2. Data governance: Metadata can be used to enforce data governance policies and to track the use of the data. This can help to ensure that the data is used in accordance with regulatory requirements and that it is protected from unauthorized access.
3. Data discovery: Metadata can be used to help users discover the data that is stored in a database. For example, it can be used to create a catalog of the data that is stored in the database, making it easier for users to find the data they need.
4. Data lineage: Metadata can be used to track the lineage of data and to understand how it is transformed and moved through different systems. This is especially useful when data is used for analytics, where it is important to understand the source and quality of the data.
5. Data Interoperability: Metadata can be used to facilitate the interoperability of data across different systems by providing a common understanding of the data and its structure.

3 Types of Database Metadata

There are several types of database metadata, but some common ones include:

Structural Metadata:

This type of metadata provides information about the structure of the data in the database, such as the names of tables, columns, and indexes. Structural metadata is used to describe the physical organization of the data, such as the location of files and the storage format.

Logical Metadata:

This type of metadata provides information about the logical organization of the data, such as the relationships between tables and the business rules that govern the data. Logical metadata is used to describe the conceptual organization of the data, such as the business terms and concepts used to describe the data.

Administrative metadata:

This type of metadata provides information about how the data is managed and administered, such as the security settings, data quality rules, and backup and recovery procedures. Administrative metadata is used to describe how the data is protected, maintained and how it’s used.

Database Metadata Examples

Here are a few examples of how metadata can be used in a database:

Table Metadata

The names, data types, and constraints for columns in a table. For example, a table named “Customers” might have columns for “CustomerID” (integer, primary key), “FirstName” (varchar), and “LastName” (varchar)

Index Metadata

Information about the indexes that have been created on a table. For example, an index on the “LastName” column of the “Customers” table might be created to improve query performance.

Constraint Metadata

Information about the constraints that have been defined on a table, such as primary keys, foreign keys, and check constraints. For example, a foreign key constraint on the “Orders” table might reference the “CustomerID” column in the “Customers” table to enforce referential integrity.

View Metadata

Information about the views that have been created on a table. For example, a view named “TopCustomers” might be created to show the top 10 customers based on their total purchase amount.

Stored Procedure Metadata

Information about the stored procedures that have been created in the database. For example, a stored procedure named “UpdateCustomerAddress” might be created to update the address of a customer in the “Customers” table.

User Metadata

Information about the users who have access to the database, including their roles and permissions. For example, a user named “JohnDoe” might have permissions to read and write to the “Customers” table, but only read access to the “Orders” table.

Backup Metadata

Information about the backups that have been taken of the database, including the date and time of the backup, and the location of the backup files.

Performance Metadata

Information about the performance of the database, including query execution times, number of rows returned, and other performance statistics.

It’s worth noting that the kind of metadata that is available and the way it is stored can vary depending on the type of database management system(DBMS) used.

How to Store Metadata in Database?

There are several ways to store metadata in a database, depending on the specific requirements of the application. Some common methods include:

Data Dictionary

A data dictionary is a set of tables that are used to store metadata about the structure of the database. These tables typically include information about the tables, columns, indexes, and constraints in the database. Data dictionaries are often built into the database management system and can be queried using SQL.

Separate Metadata Repository

Another approach is to store metadata in a separate metadata repository, which is a database that is specifically designed to store metadata. This approach allows for more flexibility and control over the metadata, but it also requires additional resources to maintain the metadata repository.

Data Modeling Tool

A data modeling tool can be used to create a data model of the database and its metadata, which can then be stored in the database. This approach allows for the metadata to be visualized and managed in a more intuitive way.

Extended Properties

Some database management systems allow for metadata to be stored as extended properties of the tables, columns, and other objects in the database. This approach allows for the metadata to be stored directly with the data, making it more accessible and easier to manage.

XML or JSON Files

Some database management systems allow to store metadata in external files like XML or JSON. This approach allows for more flexibility and portability of the metadata, as it can be easily shared and understood across different systems.

More to Read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• Data Warehouse vs Database
• Dataset vs Database
• Database vs DataFrame
• Postgres Schema vs Database
• Relational Database vs Flat File
• Primary Key vs Foreign Key
• Primary Key vs Candidate Key
• Document Database Vs. Key Value Store
• Document Database Vs. Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Graph Databases
• Document Database
• Centralized Database

A primary key is a unique identifier for a specific row in a table in a database. It is used to ensure data integrity and to prevent duplicate entries. It can be one column or a combination of multiple columns.

It is used to quickly look up and retrieve specific rows from the table. Primary keys must contain unique values and cannot contain null values.

Here are three examples of how a primary key might be used:

1. In a database for a retail store, the id field in the customers table could be the primary key. This would ensure that each customer in the table is assigned a unique identification number, which could be used to quickly look up information about a particular customer.
2. In a database for a library, the book_id field in the books table could be the primary key. This would ensure that each book in the library’s collection is assigned a unique identification number, which could be used to quickly look up information about a particular book.
3. In a database for a school, the student_id field in the students table could be the primary key. This would ensure that each student at the school is assigned a unique identification number, which could be used to quickly look up information about a particular student.

What is a Foreign Key?

A foreign key is a field in a database table that is used to uniquely identify a row in another table. Foreign keys are used to establish and enforce relationships between tables in a database, such as the relationship between a customer and an order or a product and a supplier.

Here are three examples of how a foreign key might be used:

1. In a database for a retail store, the customer_id field in the orders table could be a foreign key that references the id field in the customers table. This would ensure that every order in the orders table is associated with a valid customer in the customers table.
2. In a database for a library, the book_id field in the borrowers table could be a foreign key that references the id field in the books table. This would ensure that every book borrowed by a patron is a valid book in the library’s collection.
3. In a database for a school, the student_id field in the grades table could be a foreign key that references the id field in the students table. This would ensure that every grade in the grades table is associated with a valid student at the school.

Primary Key Vs Foreign Key

• A primary key is a column or set of columns that uniquely identifies each row in a table. This means that no two rows in the table can have the same primary key value while a foreign key is a column or set of columns in one table that refers to the primary key of another table.
• The primary key is used to enforce the integrity of the data and to quickly look up and retrieve specific rows from the table and the purpose of a foreign key is to establish a link between the data in two tables, so that changes in one table will affect the data in another table.
• Primary keys must contain unique values and cannot contain null values. The primary key is also used as a reference point for other tables in the database, through the use of foreign keys while foreign key is used to enforce referential integrity, which ensures that data in one table is consistent with data in another table.
• EXAMPLE: If you have a table called “orders” and another table called “customers”, the primary key in the “customers” table would be the customer ID, and the foreign key in the “orders” table would be the customer ID, linking the order to the customer. This way when you make changes to the customer data (e.g. updating the customer address) it will also affect all the orders that were made by that customer.

More to Read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• Data Warehouse vs Database
• Dataset vs Database
• Database vs DataFrame
• Primary Key vs Foreign Key
• Primary Key vs Candidate Key
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Graph Databases
• Centralized Database

Most Popular NoSQL Databases

There are many different NoSQL databases available, each with its own unique set of features and capabilities. Some of the most popular NoSQL databases include:

MongoDB

MongoDB is a popular NoSQL database that is based on the document store model, which means that data is stored in documents that are similar to JSON objects. This allows for the efficient representation and manipulation of complex data structures and relationships.

Cassandra

Cassandra is a NoSQL database that is based on the column store model, which means that data is organized into columns rather than rows. This allows for fast and efficient data retrieval, especially for large datasets.

Redis

Redis is an in-memory data store that is often used as a cache or message broker. It is known for its low latency and high performance, making it a popular choice for real-time applications and in-memory databases.

Couchbase

Couchbase is a document-based NoSQL database that is designed for high-performance and scalability. It is also known for its full-text search capabilities and its ability to handle unstructured data.

AWS DynamoDB

AWS DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS). It is known for its scalability and performance, and it’s built to handle high-traffic, real-time applications.

Cosmos DB

Cosmos DB is Microsoft’s globally-distributed, multi-model database service. It’s very popular for its scalability, performance and multiple data models support ( Document, Key-value, Graph, Column-family)

Elasticsearch

Elasticsearch is a powerful search engine based on the Lucene library. It is a distributed, JSON-based search and analytics engine designed for handling large amounts of data.

HBase

HBase is also a NoSQL database that is based on the key-value store model, which means that data is organized as a set of keys and values. This allows for fast and efficient data access and retrieval, and is well suited for applications that require real-time data access.

These are some of the most popular NoSQL databases among developers and industry practitioners, and their popularity often reflects the specific needs and use cases that they are best suited for. It’s important to understand your own needs and requirements, and to test and evaluate the different options before choosing a specific database for your application.

Types of NoSQL Database

There are different types of NoSQL databases, including the following:

Document store

Document store NoSQL databases store data in documents that are similar to JSON objects. This allows for the efficient representation and manipulation of complex data structures and relationships. Examples of document store NoSQL databases include MongoDB and CouchDB.

Column store

Column store NoSQL databases store data in columns rather than rows. This allows for fast and efficient data retrieval, especially for large datasets. Examples of column store NoSQL databases include Cassandra and HBase.

Key-value store

Key-value store NoSQL databases store data as a set of keys and values. This allows for fast and efficient data access and retrieval, and is well suited for applications that require real-time data access. Examples of key-value store NoSQL databases include Redis and DynamoDB.

Graph database

Graph database store data as a network of nodes and edges, which allows for the efficient representation and manipulation of complex data relationships. Examples of graph databases include Neo4j and ArangoDB.

Wide-column store

Wide-column store NoSQL databases store data in columns, but the columns can be dynamically added or removed, which allows for more flexibility and scalability compared to traditional column store databases. Examples of wide-column store NoSQL databases include Bigtable and Apache HBase.

Advantages of NoSQL Database

Scalability and flexibility

One of the main advantages of NoSQL databases is that they are highly scalable and flexible, which means that they can easily handle large volumes of data and support a large number of users and applications. This is particularly useful for organizations that experience sudden spikes in traffic or data volume.

Improved performance

NoSQL databases can have improved performance compared to traditional relational databases, especially for large datasets or applications that require real-time data access. This is because NoSQL databases are optimized for fast and efficient data retrieval and manipulation, and are not limited by a fixed schema.

Support for unstructured and semi-structured data

NoSQL databases are well suited for handling unstructured and semi-structured data, which is data that does not have a fixed schema or data that is not easily organized into rows and columns. This is particularly useful for applications that require the integration of different data types and sources, such as multimedia data or data from multiple sources.

Ease of use and development

NoSQL databases are typically easier to use and develop as compared to traditional relational databases, especially for developers who are not familiar with SQL. This can reduce the time and resources required to develop and maintain applications that use a NoSQL database.

Cost savings

NoSQL databases can help organizations to reduce costs, since they do not require the upfront investment in hardware and infrastructure that traditional relational databases do. Additionally, organizations only pay for the resources they use, which can help to control costs and avoid overspending.

Disadvantages of NoSQL Database

Limited support for SQL

One of the main disadvantages of NoSQL databases is that they do not support SQL, which is the standardized and widely used language for managing and querying data in relational databases. This can limit their compatibility with other systems and applications that use SQL, and may require developers to learn and use specialized NoSQL query languages.

Limited support for ACID transactions

NoSQL databases typically do not support ACID (Atomicity, Consistency, Isolation, Durability) transactions, which are a set of properties that guarantee the integrity and consistency of data in a database. This can limit their ability to handle complex data manipulation operations, such as data aggregation or data mining.

Limited vendor support

NoSQL databases are not as widely used as traditional relational databases, which means that they may have limited vendor support and resources compared to other database models. This can make it more difficult to find support and expertise for NoSQL databases.

Potential vendor lock-in

Organizations that use a NoSQL database may be dependent on their database vendor, which can create vendor lock-in and limit their ability to switch to another database in the future.

Compatibility issues

NoSQL databases may not be compatible with other database models, which can limit their interoperability and integration with other systems and applications.

Use Cases of NoSQL Database

NoSQL databases are a popular choice for many modern applications because they offer several benefits over traditional relational databases.

So, when to use a NoSQL database? Here are common use cases for NoSQL databases:

1. Storing and processing large amounts of data, such as in the case of big data applications: NoSQL databases are designed to scale horizontally, which means they can easily handle large volumes of data without sacrificing performance.
2. Storing and managing unstructured data, such as documents, images, and videos: NoSQL databases are typically more flexible than relational databases, which makes them well-suited for handling unstructured data.
3. Building real-time, high-performance applications, such as mobile and web applications: NoSQL databases are generally faster and more efficient than relational databases, which makes them a good choice for applications that require quick response times.
4. Mobile and IoT applications: NoSQL databases are often used to store and process data from mobile and Internet of Things (IoT) devices, due to their ability to handle a high volume of read and write operations in real-time.
5. Enabling rapid development and deployment of applications: Because NoSQL databases are typically more flexible and scalable than relational databases, they can make it easier and faster to develop and deploy modern applications.
6. Supporting cloud-native architectures and applications: NoSQL databases are often used in cloud-based applications because they are designed to be distributed and scalable, which makes them well-suited for the dynamic nature of cloud environments.

Which is the Fastest NoSQL Database?

When it comes to performance, NoSQL databases have some inherent characteristics that make them particularly fast, such as horizontal scaling and distributed architecture. However, the specific performance of a NoSQL database will depend on a variety of factors, including the size and complexity of your data, the number of concurrent users and requests, and the specific implementation and configuration of the database.

With that said, some NoSQL databases are known for their high performance and are commonly used in high-traffic and high-performance applications. Some examples of NoSQL databases include:

• Redis: Redis is an in-memory data store that is often used as a cache or message broker. It is known for its low latency and high performance, making it a popular choice for real-time applications.
• Aerospike: Aerospike is a distributed NoSQL database that is optimized for high performance and low latency. It is designed for high-traffic, real-time applications and it’s known for his high performance and scalability.
• Cassandra: Cassandra is a highly-scalable, distributed NoSQL database that is optimized for read and write performance. It’s been used in high-performance applications that require low-latency data access, and it’s known for it’s linear scalability.
• MongoDB: MongoDB is a document-based NoSQL database that is known for its high performance and scalability. It also provides built-in sharding and automatic balancing of data, making it a good choice for high-traffic, real-time applications.

Keep in mind that the specific performance of a NoSQL database will depend on a variety of factors and it’s always recommended to test and validate the performance of the database under your specific use case, workload and data size.

It’s also worth to note that, performance is not the only factor to take into account when choosing a database, you should also consider the specific requirements of your use case, your expertise with the database and your team skills to manage and maintain the database.

More to Read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Graph Databases
• Centralized Database

What is a Graph Database?

In a graph database, the data is stored as a set of vertices and edges, with each vertex representing an entity (such as a person or a business) and each edge representing a relationship between two vertices (such as a friendship or a business partnership). The graph structure allows for flexible and efficient querying, as it allows for traversing relationships between entities in various ways.

Graph databases are particularly useful for storing and querying data that has complex relationships, such as social networks, recommendation engines, and fraud detection systems. They are also often used in areas such as bioinformatics and supply chain management, where the data has a complex, interconnected structure.

Graph Database Use Cases

There are many use cases for graph databases, as they are particularly well-suited for storing and querying data that has complex relationships. Some common use cases for graph databases include:

Social networks

Graph databases can be used to store and query data about relationships between people, such as friendships, family relationships, and professional connections. This can be used to build social networking platforms, recommendation engines, and other applications.

Fraud detection

Graph databases can be used to identify patterns of fraudulent activity by analyzing the relationships between entities such as individuals, businesses, and transactions.

Recommendation engines

Graph databases can be used to store and query data about users and their interactions with products or content. This can be used to build recommendation engines that suggest products or content to users based on their interests and past behavior.

Supply chain management

Graph databases can be used to store and query data about the relationships between different entities in a supply chain, such as suppliers, manufacturers, and retailers. This can be used to optimize logistics and supply chain management.

Bioinformatics

Graph databases can be used to store and query data about the relationships between different biological entities, such as genes, proteins, and diseases. This can be used to study the relationships between different biological processes and to develop new drugs and treatments.

Graph Database List

Some popular graph databases are:

Neo4j

Neo4j is a widely used open-source graph database that is optimized for storing and querying large amounts of data. It is written in Java and supports ACID transactions, making it suitable for use in enterprise applications.

JanusGraph

JanusGraph is an open-source, distributed graph database that is built on top of Apache Cassandra and Elasticsearch. It is designed to handle large-scale graph data and is optimized for high performance and scalability.

Amazon Neptune

Amazon Neptune is a fully managed graph database service that is optimized for storing and querying graph data. It is designed to be easy to use and is backed by the reliability and security of the Amazon Web Services (AWS) cloud platform.

OrientDB

OrientDB is an open-source, multi-model database that supports graph, document, key-value, and object data models. It is designed to be scalable and efficient, and it supports ACID transactions andSQL-like query language.

ArangoDB

ArangoDB is an open-source, multi-model database that supports graph, document, and key-value data models. It is designed to be flexible and easy to use, and it supports ACID transactions and a powerful query language.

Graph Database Properties

Graph databases have several properties that make them unique and well-suited for storing and querying data that has complex relationships:

Flexible data model

Graph databases use a flexible data model that allows for the representation of complex relationships between entities. This makes it easy to store and query data that has many different types of relationships and connections.

Efficient querying

Graph databases are optimized for efficient querying of data, particularly when it comes to traversing relationships between entities. This makes it easy to find and retrieve data about specific entities and their relationships with other entities.

Scalability

Graph databases are designed to scale well as the size of the data increases. This makes them suitable for storing and querying large amounts of data.

ACID transactions

Many graph databases support ACID (Atomicity, Consistency, Isolation, Durability) transactions, which ensure that data is stored and accessed in a consistent and reliable manner. This is important for applications that require high levels of data integrity and reliability.

High performance

Graph databases are optimized for high performance and can handle a large number of queries and updates in real-time. This makes them suitable for use in high-traffic applications.

Graph Database Vs. Relational Database

Graph databases are based on graph theory and are designed to store and process complex relationships and connections between data. They are particularly well suited for data that is hierarchical, connected, or has complex relationships.

Relational databases are based on the relational model and are designed to store and process structured data. They are particularly well suited for data that can be organized into tables with well-defined relationships between the rows and columns.

There are a few key differences between graph databases and relational databases:

In general, graph databases and relational databases each have their own strengths and weaknesses, and the choice between the two will depend on the specific needs of your application.

More to read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Centralized Database

What is cloud database?

A cloud database is a type of database that is hosted on a cloud computing platform, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. This means that the database is not stored on local servers or hardware, but is instead accessed and managed over the internet. Cloud databases offer several advantages over traditional on-premises databases, including increased scalability, flexibility, and reliability.

Cloud databases can be accessed and managed by users and applications from anywhere, as long as they have an internet connection. This makes them ideal for organizations that need to manage large amounts of data, or that need to access their data from multiple locations. Cloud databases are also typically more reliable and scalable than on-premises databases, since they are managed by cloud service providers that have the expertise and resources to ensure high availability and performance.

Why We Need Cloud Database?

There are several reasons why organizations may need a cloud database, including the following:

To increase scalability:

Cloud databases are highly scalable, which means that they can easily handle large amounts of data and support a large number of users and applications. This is particularly useful for organizations that experience sudden spikes in traffic or data volume.

To improve flexibility:

Cloud databases are highly flexible, which means that organizations can easily add or remove resources as needed. This is particularly useful for organizations that need to be able to quickly adapt to changing business needs or requirements.

To enhance reliability:

Cloud databases are typically more reliable than on-premises databases, since they are managed by cloud service providers that have the expertise and resources to ensure high availability and performance.

To reduce costs:

Cloud databases can help organizations to reduce costs, since they do not require the upfront investment in hardware and infrastructure that on-premises databases do. Additionally, organizations only pay for the resources they use, which can help to control costs and avoid overspending.

To support remote access:

Cloud databases can be accessed and managed by users and applications from anywhere, as long as they have an internet connection. This is particularly useful for organizations that need to access their data from multiple locations or that have a distributed workforce.

Popular Cloud Databases

There are several popular cloud databases available, each with its own strengths and use cases. Here is a list of some of the most widely used cloud databases:

Amazon Relational Database Service (RDS)

Amazon RDS is a fully managed database service that makes it easy to set up, operate, and scale a relational database in the cloud. It supports a variety of database engines, including MySQL, PostgreSQL, and Oracle.

Google Cloud SQL

Google Cloud SQL is a fully managed database service that makes it easy to set up, operate, and scale a MySQL database in the cloud. It offers high performance, scalability, and security.

Azure SQL Database

Azure SQL Database is a fully managed database service that makes it easy to set up, operate, and scale a relational database in the cloud. It supports a variety of database engines, including SQL Server, MySQL, and PostgreSQL.

MongoDB Atlas

MongoDB Atlas is a fully managed database service that makes it easy to set up, operate, and scale a MongoDB database in the cloud. It offers high performance, scalability, and security.

Amazon DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service that makes it easy to set up, operate, and scale a non-relational database in the cloud. It offers high performance, scalability, and reliability.

Google Cloud Firestore

Google Cloud Firestore is a fully managed NoSQL database service that makes it easy to set up, operate, and scale a non-relational database in the cloud. It offers real-time updates and offline support.

Azure Cosmos DB

Azure Cosmos DB is a fully managed database service that makes it easy to set up, operate, and scale a NoSQL database in the cloud. It offers a variety of data models and APIs, including support for MongoDB, Cassandra, and Azure Table Storage.

Ultimately, the best cloud database for your use case will depend on your specific requirements, such as the type of data you are storing, the performance and scalability needs of your application, and your budget.

Cloud Database Advantages

1. Increased scalability: One of the main benefits of using a cloud database is that it is highly scalable, which means that it can easily handle large amounts of data and support a large number of users and applications.
2. Improved flexibility: Cloud databases are highly flexible, which means that organizations can easily add or remove resources as needed. This allows them to quickly adapt to changing business needs and requirements.
3. Enhanced reliability: Cloud databases are typically more reliable than on-premises databases, since they are managed by cloud service providers that have the expertise and resources to ensure high availability and performance.
4. Reduced costs: Cloud databases can help organizations to reduce costs, since they do not require the upfront investment in hardware and infrastructure that on-premises databases do. Additionally, organizations only pay for the resources they use, which can help to control costs and avoid overspending.
5. Support for remote access: Cloud databases can be accessed and managed by users and applications from anywhere, as long as they have an internet connection. This is particularly useful for organizations that need to access their data from multiple locations or that have a distributed workforce.

Cloud Database Disadvantages

While there are many benefits to using a cloud database, there are also some disadvantages that organizations should consider, including the following:

1. Security concerns: One of the main disadvantages of cloud databases is that they may raise security concerns, since the data is not stored on local servers or hardware. Organizations need to carefully consider the security measures and policies of their cloud service provider to ensure that their data is protected.
2. Dependence on internet connectivity: Cloud databases require an internet connection to be accessed and managed, which means that organizations may not be able to access their data if there is an internet outage or other connectivity issues.
3. Limited control: Since cloud databases are managed by a third-party cloud service provider, organizations may have limited control over their data and how it is accessed and managed.
4. Potential vendor lock-in: Organizations that use a cloud database may be dependent on their cloud service provider, which can create vendor lock-in and limit their ability to switch to another provider in the future.
5. Compliance issues: Organizations that are subject to regulatory or compliance requirements may face challenges when using a cloud database, since they need to ensure that their data is handled and stored in a compliant manner.

More to read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Centralized Database

A primary key is a field or set of fields in a database table that uniquely identifies each record in the table. Primary keys are used to ensure that each record in a table is unique and can be easily identified.

A foreign key is a field or set of fields in a database table that refers to the primary key of another table. Foreign keys are used to establish relationships between tables, allowing data from multiple tables to be linked and queried together.

Here’s an example to illustrate how primary keys and foreign keys work:

Imagine you have a database with two tables: a “Customers” table and an “Orders” table. The “Customers” table contains information about each customer, such as their name, address, and phone number. The “Orders” table contains information about each order, such as the order number, the customer who placed the order, and the items in the order.

In this case, the “Customer ID” field in the “Customers” table could be used as the primary key, and the “Customer ID” field in the “Orders” table could be used as the foreign key. This would establish a relationship between the two tables, allowing you to link customer information with order information.

Overall, primary keys and foreign keys are important in relational databases because they allow data from multiple tables to be linked and queried together, enabling more powerful and flexible data analysis and reporting.

Further Reading

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Object Oriented Database
• Operational Database
• Personal Database
• Centralized Database

Operational databases are typically updated in real-time, allowing the organization to quickly access and use the most current data. This type of database is often used in applications where data needs to be accessed quickly and accurately, such as in online stores or financial systems.

Operational databases are designed to be highly reliable and efficient, with a focus on supporting fast, real-time transactions and queries. They are typically updated on a regular basis and are used by multiple users and applications within the organization.

Why operational database is used?

There are several reasons why organizations use operational databases, including the following:

1. To support real-time transactions and queries: Operational databases are designed to support fast, real-time transactions and queries, which are essential for running the day-to-day operations of the organization.
2. To store and manage transactional data: Operational databases are used to store and manage the transactional data of the organization, including customer information, sales data, inventory data, and other similar information.
3. To ensure data integrity and reliability: Operational databases are designed to be highly reliable and efficient, with built-in mechanisms to ensure data integrity and accuracy. This ensures that the data stored in the database is always accurate and up-to-date.
4. To support multiple users and applications: Operational databases are typically accessed by multiple users and applications within the organization, which allows them to share data and collaborate on projects.
5. To improve decision making: Operational databases provide organizations with real-time data and insights, which can be used to make informed business decisions and improve the overall efficiency of the organization.

Operational Database Features

1. Storing and managing transactional data: Operational databases are used to store and manage the day-to-day transactional data of an organization, including customer information, product information, sales data, inventory data, and other similar information.
2. Supporting real-time transactions and queries: Operational databases are designed to support fast, real-time transactions and queries, which are essential for running the day-to-day operations of the organization.
3. Ensuring data integrity and reliability: Operational databases are designed to be highly reliable and efficient, with built-in mechanisms to ensure data integrity and accuracy. This ensures that the data stored in the database is always accurate and up-to-date.
4. Supporting multiple users and applications: Operational databases are typically accessed by multiple users and applications within the organization, which allows them to share data and collaborate on projects.
5. Providing real-time data and insights: Operational databases provide organizations with real-time data and insights, which can be used to make informed business decisions and improve the overall efficiency of the organization.

Operational Database Examples

This type of database typically contains current and up-to-date information that is used to support the daily operations of the organization.

Examples of operational databases include customer and product databases in e-commerce websites, financial transaction databases in banks, and inventory databases in manufacturing companies. These databases are often accessed and updated in real-time by multiple users in the organization, and they are typically optimized for fast access and retrieval of data.

Advantages of Operational Database

1. Real-time data and insights: Operational databases provide organizations with real-time data and insights, which can be used to make informed business decisions and improve the overall efficiency of the organization.
2. Improved data integrity and reliability: Operational databases are designed to be highly reliable and efficient, with built-in mechanisms to ensure data integrity and accuracy. This ensures that the data stored in the database is always accurate and up-to-date.
3. Enhanced collaboration and coordination: Operational databases are typically accessed by multiple users and applications within the organization, which allows them to share data and collaborate on projects.
4. Increased operational efficiency: Operational databases support fast, real-time transactions and queries, which help organizations to run their operations more efficiently and effectively.
5. Reduced data duplication and errors: Operational databases store and manage the transactional data of the organization in a centralized and standardized way, which reduces data duplication and errors. This can help to improve the overall accuracy and reliability of the data.

Disadvantages of Operational Database

1. High maintenance and operational costs: Operational databases require regular maintenance and support, which can be costly for organizations.
2. Limited scalability: Operational databases are designed to support fast, real-time transactions and queries, which can limit their scalability and flexibility.
3. Complexity and expertise required: Operational databases are typically complex and require specialized expertise to set up and maintain. This can be a challenge for organizations that do not have the necessary resources and expertise in-house.
4. Potential security risks: Operational databases store and manage sensitive transactional data, which can be vulnerable to security risks if not properly protected.
5. Compatibility issues: Operational databases may not be compatible with other systems and applications, which can limit their interoperability and integration with other systems.

Further Reading

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Personal Database
• Centralized Database

Relational databases are some of the most popular databases used today. Here are examples of relational databases, briefly explaining each one:

This table shows the summary of relational databases

Examples of Relational Database

Let’s discuss these relational databases briefly.

Microsoft SQL Server

Microsoft SQL Server is a relational database management system (RDBMS) developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network (including the Internet).

MySQL

MySQL is a widely used, open-source relational database management system (RDBMS). MySQL is free and open-source software under the terms of the GNU (General Public License) and is also available under a variety of proprietary licenses.

PostgreSQL

PostgreSQL, often simply Postgres, is an object-relational database management system (ORDBMS) with an emphasis on extensibility and standards compliance. It is free and open-source software released under the PostgreSQL License.

SQLite

SQLite is a relational database management system contained in a C programming library. In contrast to many other database management systems, SQLite is not a client–server database engine. Rather, it is embedded into the end program. SQLite is ACID-compliant and implements most of the SQL standard, using a dynamically and weakly typed SQL syntax that does not guarantee the domain integrity.

SQLite is one of the most widely deployed database engines, as it is used today by several widespread browsers, operating systems, and embedded systems, among others. Heavily used in iPhone applications, it features full-text search and geospatial query support.

Oracle Database

Oracle Corporation’s Oracle database is a relational database management system (RDBMS) that runs on major platforms like Linux, UNIX, and Windows. It is very popular relational database and is known for its scalability, reliability, and performance.

MariaDB

MariaDB is an open-source relational database management system (RDBMS) that’s compatible with the popular MySQL database. MariaDB is used by organizations of all sizes, from small businesses to some of the largest companies in the world.

IBM Informix

Informix was one of the first commercial relational database products on the market; it was designed to run on mainframe computers in 1982 but was subsequently ported to Unix, Linux, macOS and Windows platforms as well. It was acquired by IBM in 2001 along with other assets from Informix Software for $1 billion USD cash plus $250 million stock options. Its stable release date is 17 November 2020.

Apache Derby

Apache Derby is an open source relational database management system that is written in Java and runs on any platform that supports Java Runtime Environment (JRE). It is lightweight and easy to use, making it a good choice for developing small databases or prototypes.

H2 Database Engine

H2 Database Engine is another open source relational database management system written in Java that can be used in embedded or server mode. It offers a simple interface and powerful features making it a good choice for small applications or prototyping purposes.

Amazon Relational Database Service (Amazon RDS)

Amazon RDS is Amazon’s managed relational database service that supports popular databases such as MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server. Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud.

Sybase ASE

Sybase ASE (Adaptive Server Enterprise) is a relational model database server produced by Sybase Corporation (now part of SAP). It primarily runs on UNIX systems. It was ported to Microsoft Windows NT also. It’s known for its robust transactional processing capabilities.

DB2

IBM DB2 is a family of hybrid data management products designed to serve a range of workloads including transactional processing, data warehousing, business intelligence and analytics. The IBM DB2 family offers IBM Hybrid Data Management solutions for managing both structured and unstructured data – on premise or in any cloud environment.

It offers a number of features to help improve performance and scalability, including query optimization, indexing and partitioning.

Microsoft Access

Microsoft Access is a database management system (DBMS) from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools. It is a member of the Microsoft Office suite of applications, included in the Professional and higher editions and also sold separately.

Further Reading

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 9 Types of Databases
• Distributed Database
• Personal Database
• Centralized Database

Relational Model

The relational model is a data model for storing and retrieving information. It is the most widely used database model in the world, with over 90% of commercial databases using it. The term “relational” refers to how data in tables are related to one another through common field names and similar rows structure. Relational databases are made up of relations that consist of columns (or attributes) and rows (also called tuples or records).

In the relational database model, each piece of data is stored in a row (or record) and columns (or fields). Each row is linked to other rows via unique IDs. This eliminates duplication of data. The database performs automatic work here: if the ID changes at any time (e.g., due to updating), then the link will be updated accordingly.

Terminologies Used in Relational Model

1. Tables – The relational model uses the table format to save relationships. Each table has two properties: rows and columns. Rows represent records, while columns represent attributes.
2. Tuple – A single row in a table contains the record and is called tuple.
3. Cardinality: Number of rows (tuples) of the table is called cardinality.
4. Attribute – A column in the table is called attribute. These are the properties define relation.
5. Degree: Number of attributes in the table is called degree.
6. Relation Schema: A relation schema represents the name, attributes and cardinality of a single relationship between two or more entities.
7. Relation instance – Relation instance is a finite set of tuples in the RDBMS system. Relation instances never have duplicate tuples.
8. Relation key – Every row has one, two or multiple attributes, which is called relation key.
9. Attribute domain – Every attribute has some pre-defined value and scope which is known as attribute domain.

Integrity Constraints

The basic structure consists of:

• Attribute
• Value
• Relation

Attributes can be either single-valued or multi-valued, while values correspond to specific objects like strings or integers. Relationships between different entities can be represented as links where each link has its own unique identifier number called a primary key which enables retrieval from other

One example from this relational model would be an email address table where each row contains contact information such as name, company address etc., just like you might find on your computer desktop’s contacts list applet (the ones usually found at top right). You can also think about any spreadsheet document which has multiple cells separated by column headers: there are many ways these could represent different types of individuals who

The relational model, or RM for short, represents the database as a collection of related tables. The columns in these tables denote entities and their relationships to one another. For example: House has-a Street will be represented by two rows – one representing the “House” entity with three values (name=house), streetName=”Street”, address=”123 Main St.”) and other row defining an instance of its relationship to Street(“has-a”)with value (“street”).

Operations performed in Relational Model

In the relational model, four types of operations are performed.

1. Insert Operation: Insert Operation is used to save the values in a new tuple.
2. Modify/Update Operation: Modify operation updates (changes) the existing values in a tuple.
3. Search Operation: We retrieve information.
4. Delete Operation: Used to delete the tuples.

Why is it called a relational database?

The data is stored in a structured format i.e. in columns and rows. In this type of storage, we can easily access the values. It is called a relational database because the values within the same table are closely related to each other. Tables within the database can also be related to each other. We can run different queries to fetch data or some specific value from multiple tables.

What is the difference between database and relational database?

In the database, the data is stored in a file while in a relational database the data is stored in the form of tables and rows. Unlike the database, we can fetch multiple data items in a relational database.

Database, usually, have a small quantity of data while relational database can deal with large amounts of data.

List of popular relational database

A number of relational database software are available in the market. We have listed some popular ones.

1. Microsoft SQL
2. Oracle Database
3. MySQL
4. Firebird
5. IBM Db2
6. Amazon Relational Database Service (RDs)
7. PostgreSQL
8. Amazon Aurora
9. MariaDB
10. SQLite
11. Azure SQL Database
12. SAP SQL Anywhere

You can see the worldwide popularity of relational databases here.

Is Python a relational database?

No, Python is an interpreted programming language. Python isn’t a relational database, but it’s very good at extracting information from them. Pandas library (or some other library) is used to connect the python to a relational database for analysis purpose.

Further Reading

• Relational Database Benefits and Limitations
• 9 Types of Databases
• Distributed Database
• Personal Database
• Centralized Database