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Relational Database vs Non-Relational Database

Relational databases store data in tables that are related to each other through key fields. They use a structured query language (SQL) to retrieve data, and the tables are organized in a way that allows for efficient querying and indexing.

Non-relational databases, on the other hand, store data in a variety of formats, including document oriented, key-value, graph, and column-family models. They are designed to handle unstructured or semi structured data, and they offer flexibility in terms of data schema and scaling.

Examples of Relational databases:

  • MySQL

  • PostgreSQL

  • Oracle Database

  • Microsoft SQL Server

  • SQLite

Examples of Non-relational databases:

  • MongoDB

  • Cassandra

  • Couchbase

  • Amazon DynamoDB

  • Apache HBase



Relational vs Non-Relational Database:

DBMS

SQL Database

NoSQL Database

Type

Relational

Non-Relational

Structure

Data is stored in tables with pre-defined columns and relationships between them

NoSQL databases do not have a fixed schema and are designed to handle unstructured data, such as text, images and video.

Schema

Fixed

Dynamic

Scalability

Vertical approach

Horizontal approach

Database Transaction

ACID properties (Atomicity, Consistency, Isolation and Durability)

Transactions are often not supported or are implemented differently, depending on the type of database.

Priority

Prioritize consistency, data integrity, complex queries and transactions

Prioritize scalability, performance and flexibility

Query Language

It used SQL to query data

It uses a variety of query languages depending on the type of database

Consistency

Strong consistency

Eventual consistency

Pros of Relational Database:

  • Relational databases provide strict transactional integrity through the use of the ACID (Atomicity, Consistency, Isolation, Durability) properties, which ensure that data is consistent and durable even in the face of hardware or software failures.

  • Relational databases enforce a well-defined schema that defines the structure, relationships, and constraints of the data, which makes it easier to query and analyze data.

  • Relational databases support SQL (Structured Query Language), which is a powerful language for querying, filtering, sorting, and aggregating data across multiple tables.

  • Relational databases provide built-in mechanisms for ensuring data integrity, such as constraints, triggers, and foreign keys, which help prevent data inconsistencies and security breaches.


Cons of Relational Database:

  • Relational databases can be difficult to scale horizontally because of their strict schema and transactional model, which can lead to performance bottlenecks and complexity.

  • Relational databases can be costly to license and maintain, and they require specialized skills and expertise to set up and administer.

  • Relational databases are not well-suited for handling unstructured or semi-structured data, such as multimedia files, text documents, or social media feeds.

  • Relational databases are not optimized for real-time processing or high- volume, low-latency workloads, which require specialized architectures and technologies.


Pros of Non-Relational Database:

  • Non-relational databases are designed for horizontal scalability and can handle large-scale, high-velocity workloads with ease.

  • Non-relational databases allow for flexible and dynamic data models, which can handle unstructured or semi-structured data more efficiently than relational databases.

  • Non-relational databases are optimized for high-performance read and write operations, making them ideal for real-time processing and low- latency applications.

  • Non-relational databases are often open-source and free to use, and they require less hardware and maintenance costs than relational databases.


Cros of Non-Relational Database:

  • Non-relational databases do not provide the same level of transactional integrity as relational databases, which can lead to data inconsistencies or loss in certain situations.

  • Non-relational databases typically do not support SQL or have limited querying capabilities, which can make it difficult to analyze and report on data.

  • Non-relational databases do not provide the same level of built-in data integrity and security mechanisms as relational databases, which can lead to security vulnerabilities or data breaches.

  • Non-relational databases often have their unique data models and query languages, which can lead to vendor lock-in and a lack of standardization across the industry.


Choice of Relational Database:

  • MySQL: Best for small to medium-sized applications, web-based applications, and data-driven websites that require a simple and easy-to-use database management system. It is also widely used in the LAMP (Linux, Apache, MySQL, PHP/Python/Perl) stack.

  • PostgreSQL: Best for complex and large-scale applications, enterprise-level systems, and data warehousing that require advanced features, such as triggers, stored procedures, and transactional integrity. It is also known for its reliability, scalability, and support for various data types.

  • Oracle Database: Best for mission-critical applications, high-availability systems, and large enterprises that require a robust and scalable database with advanced features, such as partitioning, clustering, and data compression. It is also known for its security and performance optimizations for Oracle applications.

  • Microsoft SQL Server: Best for Windows-based applications, .NET development, and enterprise-level systems that require a reliable and scalable database with built-in support for business intelligence, data warehousing, and analytics.

  • SQLite: Best for embedded systems, mobile applications, and lightweight web applications that require a self-contained and portable database with minimal setup and administration.


Choice of Non-Relational Database:

  • MongoDB: Best for high-velocity, high-volume, and complex data sets that require flexible and scalable data models, such as social media, IoT, and real-time analytics. It is also known for its ease of use, developer productivity, and support for dynamic schema.

  • Cassandra: Best for distributed systems, multi-data center deployments, and high- write throughput workloads that require linear scalability and fault tolerance. It is also known for its tunable consistency and support for large-scale data sets.

  • Couchbase: Best for high-performance, low-latency, and high-availability applications that require fast data access and consistent replication. It is also known for its support for JSON documents, mobile synchronization, and in- memory caching.

  • Amazon DynamoDB: Best for serverless applications, AWS cloud services, and web-scale applications that require a fully managed and scalable NoSQL database with low-latency access, automatic scaling, and built-in security.

  • Apache HBase: Best for big data applications, machine learning, and real-time analytics that require a distributed and scalable database with support for unstructured data and low-latency access. It is also known for its integration with the Hadoop ecosystem and support for Apache Phoenix SQL.


Conclusion:

Choosing the proper database is not a straight and precise decision, even for experts. Deciding whether to go for relational or non-relational databases is a great way to start. Still, it is also essential to consider the many SQL and NoSQL options available in the market.

For instance, for a lot of unstructured data, CouchDB or MongoDB can be a good option, but maybe for high availability, Redis and Cassandra might be more suitable. And these are all non-relational database systems.

On the other hand, SQL databases offer many advantages regarding data transactions and overall data integrity. Moreover, relational databases relationships can be easily identified and defined, making it straightforward to identify critical insights.


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