Collecting, storing, and using data has become the foundation of many businesses across industries. As we move into an increasingly digital society, the volume and type of data that can be collected are growing yearly. More and more companies are making digital transformations to keep up with the evolving data demands and remain competitive and agile. Case in point: Our post on exploring emerging trends and technologies related to Salesforce outlined how the company’s success is due to its competency in consolidating customer data, which allows it to make data-driven decisions using analytics.
Behind every data-driven decision is a database that gives the company the ability to use data to improve its services. NoSQL databases are becoming increasingly popular across industries due to their flexibility, capacity to scale, ability to integrate with existing platforms, and support for new technologies like artificial intelligence (AI) and machine learning (ML).
The global NoSQL database market is predicted to be worth $86.3 billion by 2032, growing at a CAGR of 28.1% from 2023 to 2032. One reason for this rapid growth is that there are different types of NoSQL databases that companies can use: document, key-value, wide-column, and graph.
In this article, we will focus on the document database and why it is widely used.
What is a Document Database?
A document database (also called a document-oriented database or a document store) is a NoSQL database that stores data in documents. The guide to document databases on MongoDB explains how these documents are much more flexible than fixed rows and columns, which is why they have become the most popular alternative to tabular, relational databases. Document databases are considered general-purpose databases that can be used in various use cases and industries.
Documents
Documents are records of information in a document database. They typically store information about one object and any of its related metadata. Documents store data in field-value pairs, and the values can be of various types and structures, such as strings, numbers, dates, arrays, or objects. A key advantage of the document database is that documents can be stored in formats like JSON, BSON, and XML, and they often include nested data structures, making it easier to store and query complex data. The structure of the documents can differ from one document to another and is one of the major features of a document database as it allows it to have a more flexible and dynamic schema. As a result, document databases are good for storing semi-structured data, which is why they are often used in web and mobile applications.
Below is a JSON document example from MongoDB that stores information about a user named Tom and his business.
{
"_id": 1,
"first_name": "Tom",
"email": "tom@example.com",
"cell": "765-555-5555",
"likes": [
"fashion",
"spas",
"shopping"
],
"businesses": [
{
"name": "Entertainment 1080",
"partner": "Jean",
"status": "Bankrupt",
"date_founded": {
"$date": "2012-05-19T04:00:00Z"
}
},
{
"name": "Swag for Tweens",
"date_founded": {
"$date": "2012-11-01T04:00:00Z"
}
}
]
}Collections
A collection is a group of documents typically storing similar content. Document databases have flexible schemas, meaning the documents in a collection don’t have to have the same fields. This allows the database to store a wide variety of information in one place without the need to create separate collections for differing data points.
Advantages of Document Databases
The advantages of a document database are:
Flexible schema – This allows the data model to be changed as an application’s requirements change and handle unstructured or semi-structured data.
Scalability – Document databases are distributed, allowing for horizontal scaling and global data distribution. The document model is also a superset of other data models, which allows them to store different data types easily.
High performance – Document databases are designed for fast read and write performance. This allows them to work without issue with large volumes of data, especially for AI and ML applications.
Able to Integrate With Existing Applications – Most document databases come with application programming interfaces (APIs), a set of rules or protocols that enables software applications to communicate with each other, allowing them to seamlessly integrate with existing systems and applications.
Use Cases of Document Databases
A DatabaseTown feature on document databases lists several use cases. These include:
Content management systems – to manage large amounts of content such as articles, blog posts, and images.
E-commerce and retail – to store product information, customer data, and order history.
Financial services – for financial data, such as transactions and account information.
Other sectors that use document databases include social media, gaming, and IoT.
For more information on the latest trends, do read the other articles in our Tech section.
