What Is a Document Database? Fundamentals You Should Know

Imagine searching through hundreds of filing cabinets for one particular record.

This isn’t a situation you want to picture yourself in, and neither do businesses. That’s why organizations use databases to store, retrieve, and manage large volumes of data.

Databases have changed since their creation in the 1960s. Relational databases became popular in the 1980s and increasingly dominant by the 2000s. Software developers used a structured query language (SQL) to store and retrieve data across cross-linked tables in these databases. However, relational databases failed to keep up with the heavy workloads, and even the most expensive hardware couldn’t help.

A non-SQL (NoSQL) database became the preferred option for organizations with scaling needs. These databases are non-relational and don’t require a fixed schema.

Document databases became one of the major types of NoSQL databases over time. They offer a fast, intuitive, and flexible schema that enables software developers to evolve data models with changing application needs.

What are documents in a document database?

A document refers to a self-describing record in a document database. Here's an example of what a document looks like in a document database.

Documents store information about objects and related metadata in field-value pairs. The values include strings, dates, arrays, objects, and numbers. The defining characteristics of a document are as follows:

Collections

A collection is a group of records that stores similar documents. Think of collections as tables in a relational database management system (RDBMS) and documents as rows. Since document databases have a flexible schema, each document doesn’t need to contain the same fields to be a part of a collection.

Every document will have a similar structure, but that isn’t necessary for the stable performance of a document database. Unlike a relational database, document database software enables you to save multiple documents with different schemas with no changes to the database itself. It’s worth noting that some document databases may lock down schema for validation purposes.

CRUD operations

Software developers rely on an application programming interface (API) or query language to execute create, read, update, and delete (CRUD) operations.

Create

Databases enable you to create documents with keys or unique identifiers (UID). A document database uses a string, path, or uniform resource identifier (URI) as a key to store and retrieve documents. Document database systems retain a key index to speed up the retrieval process and may require keys to add a document to the database.

Read

Developers often use API or query language to retrieve or read documents from a database. They can also leverage key-to-document lookup to find documents based on their UIDs or field values. You can also improve the read performance by adding indexes to the database.

Update

You can update existing documents or document metadata in a database by changing them individually or replacing them with new information.

Delete

The delete operation uses the same syntax as read operations to delete documents from a single collection. Some document databases even allow you to set filters and specific criteria for deleting documents.

Features of document databases

Document database software systems enable organizations to access data immediately with fast queries and flexible indexing. The flexibility of using the same document model for application coding and data query makes document database systems even more lucrative for information technology (IT) companies. Here’re the features that make organizations choose document databases over SQL databases.

Intuitive document data model

Document databases store data using documents instead of structures, like tables or graphs. Programming languages map these documents to objects via coding and enable you to store data together so you can access them together. This flexibility allows developers to write less code and still deliver stellar end-user performance.

Document databases empower developers to create applications rapidly. They eliminate the need to integrate separate object-relational mapping (ORM) layers, run expensive joins, or decompose data across tables.

Document databases using JSON documents for data storage enable you to structure data using rich objects, key-value stores, graph nodes and edges, and geospatial or time-series data. This data modeling helps you create easy-to-access, language-independent, lightweight, and human-readable documents.

Flexible schema

Document databases come with dynamic and self-describing schemas (implementation of a data model in a specific database) that offer you the flexibility to have documents with different fields in a collection. This ability to accommodate varying fields across documents eliminates the need for pre-defining schemas in a database.

When developers don’t have to pre-define schemas, they can easily modify structures without causing disruptions during schema migration. Some document databases come with a schema validation feature that allows you to enforce document structure rules and optionally lock down schemas.

Horizontal scaling and resiliency

Document stores facilitate horizontal scaling or scale-out, enabling you to add nodes to share the data load. This allows you to spread data across nodes without requiring queries to join nodes together, making data distribution easier.

Furthermore, document databases support replication and partitioning or sharding, both of which help you to scale database performance.

Easy querying

Document databases ease the CRUD operation execution by letting developers query through an API or query language. This ease of querying translates into easy data retrieval using field values or unique identifiers.

Why use a document database

Document databases offer several compelling advantages, making them an attractive choice for many applications.

Their schema flexibility allows for dynamic adjustments to data structures without disrupting existing records, which is ideal for environments where requirements frequently change. This flexibility, combined with the ability to handle complex data models, enables developers to represent real-world entities more naturally.

Document databases are designed for scalability, allowing them to efficiently manage large volumes of data by distributing it across multiple servers, which is crucial for high-traffic applications. Additionally, they enhance read and write performance by storing related data in a single document, reducing the need for complex joins.

When to use a document database

Document databases are ideal for applications with varying data structures, such as user profiles, product catalogs, and content management systems, where different entities may have distinct attributes.

They are also advantageous in environments focused on real-time analytics or those managing large volumes of unstructured or structured data, such as IoT applications and social media platforms.

If your project demands quick iterations and the ability to evolve data models easily, a document database would be a strong choice.

How does a document database work?

A document database software stores or fetches information in the form of a document or semi-structured database. You can manage these non-relational documents based on key-value pairs instead of a tabular schema of rows and columns.

Document databases can parse documents regardless of the type of data they store. This data storage flexibility makes querying, adding, editing, and deleting easier for developers. However, you can still use different file format schemas to define document structures.

Some developers believe that document databases are less secure than SQL databases. One way to handle database security is to find source code vulnerabilities with static application security testing (SAST) software, a white box testing technique for examining code for software flaws and weaknesses. You can also use dynamic application security testing (DAST) software to find vulnerabilities in applications while they’re in production.

Document database vs. relational database vs. graph database

A document database is a non-relational, NoSQL database that stores unstructured data using flexible documents. The data model behind a document database is intuitive and relies on a flexible schema to evolve with the changing needs of an application. Developers use an API or a query language to perform CRUD operations on a document database.

A relational database uses the SQL interface to store and offer access to related data points. Relational database systems establish data relationships by connecting keys and attributes across rows and columns. The logical data structure behind a relational model allows developers to modify physical data storage without affecting its data.

A graph database (GDB) software is a NoSQL database and stores data using nodes and relationships instead of documents or tables. Nodes and edges represent data entities and relationships between nodes, respectively. These NoSQL databases treat data and data relationships equally. The data model behind a graph database enables developers to read data relationships from the storage instead of calculating and querying connection steps.

Document database use cases

Document databases are ideal for storing unstructured data such as profiles, catalogs, and large documents. You can search and access these documents using key-value pairs.

Furthermore, document database software systems record retrieval by reading documents into memory objects. Some of the common use cases of document database management systems (DBMS) are as follows.

User profiles

Online platforms that store user profile information use document databases to accommodate documents with different data values and attributes. A document database system’s ability to manage user-specific attributes makes storing user profile data easy, even with varying types of information from users.

For example, a document database can easily modify information when users add, update, or delete profile data. This individuality and fluidity make document databases the go-to choice for organizations storing large volumes of user data.

Content management

One key priority for content management system (CMS) software is aggregating content from multiple sources and sharing it among customers. Document database software caters to CMS needs by letting users easily collect, store, and manage different types of content, including user-generated content, audio, images, videos, and comments.

Business intelligence

Organizations use different environments for maintaining operational and analytical databases. As a result, they struggle with operational data extraction, which is important for gathering competitive intelligence. Document databases solve this problem by enabling organizations to manage operational data from multiple sources and feed data to business intelligence (BI) engines for analysis.

Book database

Book databases that use RDBMS store book and author data with tables. These databases don’t consider null values and require each author to have at least one book entry. Document databases solve this problem by storing an array of books for each author, meaning you have the flexibility to have authors without books.

Product catalog

Product catalogs store various product-related information, including features, product descriptions, weight, colors, dimensions, availability, and customer reviews. Organizations that store and manage thousands of products require faster reading time for a seamless user experience.

Document databases enable these organizations to store a single product in a single document for faster readability. Furthermore, you can modify product attributes without affecting other documents.

Mainframe offload

Organizations migrating from legacy mainframe systems to more modern architectures can use document databases to offload data. Document databases allow for the storage of semi-structured data from mainframe applications, providing a more agile and scalable solution for data management.

For instance, a financial institution may offload historical transaction data into a document database to facilitate easier access and analytics while reducing dependency on aging mainframe technology.

Data hub

Document databases serve as effective data hubs that integrate and unify data from various sources. By consolidating data into a single document format, organizations can streamline data access and management, making it easier to share information across different teams or applications.

A marketing analytics software can use a document database as a central repository for customer interactions, campaign data, and website activity, enhancing collaboration and insights.

Payment processing

Document databases can manage transaction records, user information, and payment details with high availability and performance in payment processing applications.

For example, an online payment gateway can store transaction documents, enabling rapid retrieval and updates while ensuring compliance with various financial regulations.

Operational analytics

Document databases are well-suited for applications requiring real-time or operational analytics. By storing event-driven data, organizations can gain insights into user behavior and operational metrics as they happen.

For instance, an online gaming platform can use a document database to capture and analyze player actions in real time, helping developers improve game balance and user engagement based on immediate feedback.

IoT data management

Document databases can efficiently store and manage data generated by IoT devices, accommodating the variability of sensor data and device metadata.

For example, a smart home application can utilize a document database to manage device states, user settings, and historical data from various connected devices, enabling seamless integration and control for users.

Customer feedback systems

Organizations can use document databases to store customer feedback, reviews, and survey responses. The flexibility of document structures allows for different feedback types, whether text, ratings, or multimedia.

For instance, a restaurant review platform can use a document database to aggregate diverse customer feedback and make it easily searchable and analyzable.

Supply chain management

Document databases can manage complex supply chain data, including inventory levels, supplier information, and shipment statuses.

For example, a logistics company can use a document database to track shipments and related documents, providing real-time updates and improving operational efficiency.

Document database benefits

Document databases ensure better performance by storing data in a single database instead of spreading it across several linked databases. Organizations with large-scale data opt for document stores because they are flexible in adding databases, scaling, and fetching analytics. Below are the benefits that contribute to the increasing popularity of document stores.

Flexibility

Document databases allow developers to control the data structure, experiment, and adapt to new requirements. You can easily add new fields or change the existing ones. This index flexibility eases database evolution with the application’s needs.

Unstructured data management

Unlike relational databases, document databases can efficiently manage unstructured data. They can even handle structured data that you’d represent with rows and columns in a relational database.

Document databases are popular because they manage unstructured data (server logs, human-generated texts, and data from different sources) that don’t follow a unified format and run complicated operations.

Scalability

Relational databases require vertical scaling, migrating data to powerful and expensive database servers for performance. However, document databases enable horizontal scaling by splitting a single database across servers.

A document database system’s scalability empowers organizations to handle large volumes of data without operational complexity. Plus, it makes it easier to distribute document data and schema across server nodes.

Open formats

You can describe documents using a variety of open formats, including JSON, XML, and other data interchange formats. You can also leverage the built-in version control to minimize conflicts as records grow in size.

Document database challenges

Some of the most common document database challenges stem from atomicity requirements, consistency, and security. Some of these challenges include:

Security

Today, data applications need to eliminate malware infections, tackle unauthorized access, maintain integrity, and preserve confidentiality for data security purposes. Relational databases handle these security issues with data authentication, authorization, database watermarking, and audit logs, while document databases need database-level security and fine-grained control.

Common security issues for document database systems include a lack of automatic data encryption, audit logs, copyright preservation, and certificate-based authentication.

Lack of consistency checking

Document database systems contain documents with varying fields. These documents may not have relations with one another. This lack of inter-relation reduces consistency checks, which cause problems during database consistency audits.

Lack of atomicity

Relational databases make data changes with a single query or command. With document databases, you have to run two separate queries to make changes in two data collections. The need to run separate queries violates atomicity requirements, meaning you’ll have to break down a requirement further to achieve the desired outcome.

Document database software

Finding the right document database software is critical when implementing seamless document management. If you’re looking to make data organization, storage, and retrieval easier, let document database software do the heavy lifting.

To be included in this category, the software must:

• Store data
• Organize data using a document model
• Allow data retrieval

*Below are the top five leading document database software solutions from G2’s Fall 2024 Grid® Report. Some reviews may be edited for clarity.

1. Amazon DynamoDB

Amazon DynamoDB is a NoSQL document database that offers single-digit millisecond performance at any scale. This document database software features built-in security, continuous backups, automated multi-region replication, in-memory caching, and data export tools.

What users like:

“The best thing about DynamoDB is DAX DynamoDB Accelerator, a fully managed in-memory cache that provides fast performance and read and write through cache. It also offers a secondary index and global secondary index.”

- Amazon DynamoDB Review, Suyash J.

What users dislike:

“There’s no easy way to run case-sensitive queries or import and export raw files. You must know all access patterns before designing the table. It’s practically impossible to make changes later. It’s also quite difficult to implement transactions, design relationships, or table joins.”

- Amazon DynamoDB Review, Sujith C.

2. MongoDB

MongoDB is a general-purpose database platform that supports transactional, search, analytics, and mobile use cases with a common query interface. Its flexible document data model enables developers to develop faster and improve performance.

What users like:

“MongoDB is a fast, document-based database that provides us with most data needs. We use MongoDB as the primary database for our application, and it proves to be very fast, robust, and easy to use. The schema-less methodology allows us to change schema as needed without worrying about past changes. The aggregate framework is a perfect solution for building complex, yet easy-to-understand, queries.”

– MongoDB Review, Nir L.

What users dislike:

“The joint operations are expensive, and we can’t use them a lot of time since it increases the time complexity. Therefore, we have to make a collective collection so that there is no need for a joint operation in the first place. But this makes our schema and database unreadable and dirty.”

– MongoDB Review, Aditya S.

3. MongoDB Atlas

MongoDB Atlas powers modern applications with a multi-cloud database service. This document database system features a document model for faster development, a unified query API for data management, and built-in security.

What users like:

“MongoDB Atlas is most suitable for a cloud-based database system where the init config and its corresponding daemon processes are handled automatically. It gives us a powerful logical volume manager (LVM), which converges our disk images from our on-premise environment to our cloud platforms. It also provides excellent hashing algorithms which create distinct string identifiers for our checksum procedures.”

- MongoDB Atlas Review, Krishnan S.

What users dislike:

“The only thing I don't like about MongoDB is that you can only create one cluster in the unpaid version. There are also space issues at times, but I guess that's how much you can get in the unpaid version.”

- MongoDB Atlas Review, Livia J.

4. Google Cloud Firestore

Google Cloud Firestore is a NoSQL document database that allows organizations to store and sync app data globally. This software is ideal for building serverless apps with strong user-based security.

What users like:

“The best part of Firestore is the use of reactive data that enables your app to find addition, deletion, or modification of any document. It helps developers create better applications and innovate the traditional way of doing things.

The free plan is helpful and ideal for developing an app that isn’t resource-heavy. If you want to implement Firestore in your project, it’s wise to use the Blaze plan. The documentation is well-explained for those making web applications.”

- Google Cloud Firestore Review, Cristian T.

What users dislike:

“Currently, Cloud Firestore doesn't have a native flutter SDK, which would have been good. Also, there are some limitations on the number of writes, which ultimately hinder scalability of write operations.”

- Google Cloud Firestore Review, Vignesh K.

5. Couchbase Server

Couchbase Server is a cloud-native, distributed database for mission-critical applications. This software combines the strengths of SQL databases with the flexibility of JSON and the scalability of NoSQL.

What users like:

“This database is straightforward and has no complex configuration. It stores data in different buckets, similar to tables in RDBMS. It also provides bucket-to-bucket sync or cluster level to diff cluster-level sync using XDCR, which helps in syncing or moving data. Couchbase eases data structuring by allowing you to save data in JSON format. It works on the N1ql query and provides suggestions for index, too.”

- Couchbase Server Review, Ashish M.

What users dislike:

“There are some issues with clustering and data replication. It doesn’t have full coherence between clusters. The dashboard isn’t very user-friendly, and it’s slow sometimes. Newbies may find it difficult to work with, as it’s not intuitive.”

- Couchbase Server Review, Illia G.

Build and scale faster with document databases

Document databases’ hierarchical, semistructured, and flexible nature enables developers to build mission-critical applications faster. Furthermore, they can leverage the flexible data model for any use case and improve performance while keeping workloads secure. That’s why organizations are increasingly adopting document database systems to manage, store, and retrieve unstructured data.

Learn the fundamentals of database management and how it can help you improve data-driven decision-making.

This article was originally published in 2022. It has been updated with new information.