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In today’s digital landscape, data is the crux for organisations across industries. In a second, zillions of pieces of data are generated globally from websites and applications. Each bit of data empowers organisations to make well-informed decisions and gain a competitive edge. Businesses can uncover valuable insights and create tailored experiences when data is utilised effectively.
To handle this large amount of data and derive insights, it is essential that businesses understand which database is suitable for them. Not only this, choosing the right database helps them be robust, agile, and scalable with ever-changing business needs.
Each database has its own strengths and weaknesses, but before we delve further into the types of databases, let us understand the simple term database.
A database is a structured way of storing data in a table in rows and columns that allows easy retrieving, manipulating, querying, updating, and deleting data.
Each row and column represents individual records and their attributes. There are two main types of databases: relational and non-relational databases.
In this article, we will explore the difference between relational and non-relational databases, examine their usage, and explore the various types within each category. Gaining a comprehensive understanding of these distinct types can empower businesses and organisations to make well-informed decisions when selecting the optimal database solution for their specific requirements.
The term relational database is based on the relational model developed by Edgar F. Codd in the 1970s. Relational database is a type of database management system which stores data in a structured manner in tables organised into rows and columns. The creation of tables within a relational database involves the use of predefined schemas that define the data types and constraints for every column. Each individual table represents an entity or a relationship between entities.
For example, consider a database of a hospital with tables for doctors, patients, and illness. Each column of the table has attributes or properties of the entities, such as the doctor’s name, patient’s name, or illness.
Some of the commonly used relational databases are:
Developed by the Microsoft Corporation, it is one of the most widely used relational database management systems. With its extensive set of features, it offers scalable, robust, and efficient data storage, retrieval, and manipulation of structured data. Microsoft SQL Server has a simple yet intuitive user interface that makes the job of a database administrator and developer easy. Its overall features make it popular among enterprise-level applications.
MySQL is an open-source RDBMS with a large community continuously contributing to its enhancement. The highly scalable platform makes it suitable for both small-scale and large web applications. It provides a reliable and scalable platform for storing and retrieving structured data and performing various operations, such as querying, updating, and managing databases. It is robust and supports multiple programming languages.
Snowflake is a cloud-based relational database management system, enabling it to be faster and more efficient in data loading and querying. It makes it ideal for making data-driven decisions and performing real-time analytics. Its separate storage and computing architecture allows scalable and elastic data processing, which helps in large-scale data warehousing and analytics. Like other RDBMS, it also supports SQL, which makes it easy for developers to handle.
Like MySQL, PostgreSQL is also an open-source relational database management system with a dedicated community of developers to help in continuous development. Being ACID-compliant makes it a robust platform with high data integrity and performance that supports concurrency. Enables data manipulation operations and writing complex queries with support for various data types, indexing options, and advanced features like triggers and stored procedures.
Non-relational databases, also known as NoSQL databases, differ from relational databases by employing a “not only SQL” approach. They are designed to handle unstructured or semi-structured data and store it in a non-tabular format. These databases offer excellent scalability and can efficiently manage large volumes of data.
Due to their flexible structure, non-relational databases can accommodate various data types such as documents, graphs, key-value pairs, and columns. They are particularly suitable for applications that generate a substantial amount of real-time data with evolving requirements.
Many real-time applications, such as IoT and sensor data, inventory and catalogue management, fraud detection, and identity authentication, generate lots of unstructured data and use non-relational databases to store and manage it.
MongoDB is one of the most popular non-relational databases. It is document-oriented and stores data in JSON-like documents. Dynamic and schema-less data modelling helps it handle semi-structured and unstructured data. Efficient indexing, sharding capabilities, and horizontal scalability make it a high-performance database suitable for web applications and real-time analytics that require high availability and scalability.
IBM Cloudant is a database as a service (DBaaS) NoSQL database managed by IBM. It supports a flexible data model, stores and retrieves data in JSON format, and is designed for handling large-scale and high-velocity data. Cloudant’s fault-tolerant storage solution and automatic scaling and load balancing properties make it a high-availability data solution. It is based on Apache CouchDB and has a distributed architecture with built-in indexing and querying capabilities. It is best suited for geographically distributed data.
DynamoDB is a NoSQL database managed by AWS. It is super fast, requires low latency, has high scalability, and can handle up to millions of requests per second. With its unique property of storing data in key-value pairs, it can support a wide range of data models. It is fault-tolerant, has on-demand capacity provisioning, and has built-in security to store and protect business data.
Apache Cassandra is a NoSQL database with multiple commodity servers that allow it to handle large amounts of data. Cassandra is a highly scalable, available, distributed, and fault tolerant database, making it suitable for data-critical applications. It can seamlessly handle increasing data volumes and have high write throughput because of its linear scalability and wide-column store data model.
SQL and NoSQL are both types of query languages used to interact with databases.
SQL or Structured Query Language is used to interact with databases where data is stored in a table in rows and columns with predefined relationships between them. NoSQL or Not Only SQL is used to interact with non-relational databases and generally do not use SQL.
SQL is used to handle structured and complex data with well defined relationships. On the other hand, NoSQL is used to handle large volumes of unstructured or semi-structured data with no predefined data structure.
SQL is used to handle data of mid-range to enterprise level applications while NoSQL is used to handle data of big data applications, real-time analytics, IoT, etc.
In conclusion, it is not about choosing the superior database or which database outperforms the other in certain scenarios. It is all about choosing the right tool for your business needs, type of data to be handled, scalability, performance, development requirements, etc. Once you are clear with that, it will become much easier for you to select the right database for your organisation.
If you are still confused about which direction to take, hire a database developer or a consultant from our vetted pool of resources, and ensure that your data management processes are efficient, scalable, and aligned with your organization’s growth objectives.
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