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The world is now undeniably a data-driven society, with every year seeing an unprecedented surge in data creation. As per the latest estimates, approximately 402.74 million terabytes of data are being generated every day, demonstrating the massive scale of digital activity taking place globally.
AI, in particular, has transformed how companies gather and use customer data. As a result of this data surge, the need for data management systems that can effortlessly store this data has become a top priority across all sectors. Currently, the most widely used database is the relational database, which has been the standard data management system for decades. However, data is changing as technology evolves, and today, 80% of all data is unstructured data such as text documents, social media posts, images, audio files, and videos. In order to effectively store this new type of data, developers are looking to NoSQL databases, specifically document databases. In this post, we will examine how document databases are different from relational databases.
Relational Databases
A relational database, also known as a relational database management system (RDMS), stores data in tables and columns. These tables often share information, forming a relationship between them, which is where a relational database gets its name. Each table contains a column that has unique values. This is referred to as the primary key. The primary key can be used in other tables to establish relationships. When the primary key from one table is used in another table, the corresponding column is known as the foreign key.
Document Databases
Document databases, also known as a document-oriented database or a document store, are a NoSQL data model. Instead of storing information in tables and columns, the data is stored in a record called a document. A document typically stores information about one object and any of its related metadata, and documents store data in field-value pairs. The values can be a variety of types and structures, including strings, numbers, dates, arrays, or objects. Document databases also support popular programming formats like JSON, BSON, and XML. When storing a group of documents, this is known as a collection. Collections typically store documents that have similar content, but not all documents in a collection are required to have the same fields. This is because document databases have flexible schemas.
Key Differences Between Relational and Document Databases
Data Storage
As mentioned above, relational and document databases store data differently. Document databases have an advantage over relational databases because, while you are bound by certain columns in a relational database, you are bound by no such limitations in a document database. As the document database contains JSON documents, which are the standard for data interchange and storage, they can have any fields you want. This allows document databases to not just be limited to structured data like a relational database, but also store unstructured data.
Scalability
Relational databases are intended to be run on a single machine, which means that if the machine’s requirements are insufficient due to data size or an increase in the frequency of access, the hardware will need to be improved. This is called vertical scaling. Document databases, on the other hand, are distributed, which allows for horizontal scaling where extra nodes are added to the database to handle increased workloads. This makes document databases much more useful for companies that are growing fast, as they won’t need to constantly invest in new hardware to keep up with their data demands.
Ease of Use
Documents map to the objects in code, which means that they are much more natural to work with. There is no need to decompose data across tables, run expensive joins, or integrate a separate Object Relational Mapping layer. The Data that is accessed together is therefore stored together, so developers don’t have to worry about manually splitting related data across multiple tables when storing it or joining it back together when retrieving it.
While relational databases are still the most widely used type of database, they are becoming overshadowed by document databases that can handle modern data demands. In the future, we could see a shift to NoSQL databases becoming the dominant database of choice.