Four Database Types You Need to Know

Databases power nearly every app, website, and system you use each day, but with so many different types available, there's always the question of which is the best for which scenario.

This guide will walk you through the most popular database types, how they work, and how to pick the best database for your needs.

Introduction to databases

Before we begin, let's define what a database is.

What is a database?

A database is an organized collection of data that can be easily accessed, managed, and updated. Think of it as a digital filing system, but far more powerful, allowing you to store vast amounts of information and retrieve it instantly with the right commands.

Databases support a wide range of applications, from your local library catalog to global social media platforms.

Databases come in the form of tools or software and are used to manage large datasets in an organized manner, allowing for efficient storage, retrieval, and analysis of information.

(Related reading: database monitoring.)

Databases vs. data warehouses vs. data lakes

With all the talk around data today, let’s clarify three common terms: databases, data warehouses, and data lakes. These are different solutions with key differences:

• Purpose: Databases handle real-time operations and transactional processing, data warehouses focus on analytical querying and reporting for business intelligence, while data lakes store raw data for advanced analytics and big data processing.
• Data structure: Databases and data warehouses store structured data with predefined schemas, but data lakes accommodate structured, semi-structured, and unstructured data.
• Performance and cost: Databases prioritize fast transactions, data warehouses emphasize query performance for analysis (often at higher costs), and data lakes offer cost-effective storage for large volumes of data, requiring additional tools for processing.

Now, back to our primary topic: databases and the various types.

Why are databases important?

Databases are essential because they help you:

• Store data efficiently so it can be retrieved quickly and accurately.
• Manage access and permissions to ensure data security and privacy.
• Scale as your business or application grows, adapting to larger amounts of data and more users.
• Maintain data integrity, ensuring accuracy and reliability, even during crashes or power outages.

These factors are crucial when running databases for businesses, organizations, or even personal use. With the rapid growth of data and the increasing need to store, manage, and retrieve it efficiently, databases are key.

Moreover, as new demands in technology arise, such as AI, newer forms of databases have emerged, such as vector databases.

Types of databases

There are several types of databases, each designed for specific needs and use cases.

The four main database types include:

1. Relational databases

Relational databases have a tabular structure, with data organized into rows and columns for easy retrieval and manipulation. Relational databases use Structured Query Language (SQL) for managing and querying data.

Relational databases are the most widely used and familiar to many developers and businesses.

How they work

Relational databases organize data into tables (columns and rows), where each table represents a different entity (for example, customers, orders, or products). Data in different tables can be related using keys.

Popular examples

• MySQL.
• PostgreSQL.
• Oracle Database.
• Microsoft SQL Server.

Strengths

• Structured data: Ideal for data with clear, consistent structure.
• ACID compliance: Ensures data integrity through Atomicity, Consistency, Isolation, and Durability.
• Powerful querying: Use SQL (Structured Query Language) for complex operations.

Best for

• Financial applications.
• Inventory systems.
• CRM systems.

Limitations

• Can struggle with unstructured data or very large, rapidly changing data.
• Traditionally limited in scalability, though modern relational databases like PostgreSQL or cloud-native solutions (e.g., AWS Aurora) have introduced features to support horizontal scaling.
• Limited flexibility for storing data of different formats.

2. NoSQL databases

A NoSQL database is a non-relational database that stores and retrieves data in different ways from traditional relational databases. It stands for "Not only SQL" and offers a more flexible approach to storing and retrieving large amounts of data. NoSQL databases are commonly used for web applications, big data projects, and other applications that require high levels of scalability.

NoSQL ("Not Only SQL") databases break away from tables and offer flexible models for handling large volumes of diverse or evolving data.

Key subtypes

• Document stores (e.g., MongoDB, CouchDB): Store data as JSON or BSON documents.
• Key-value stores (e.g., Redis, DynamoDB): Store pairs of keys and values, great for caching and metadata.
• Wide-column stores (e.g., Cassandra, HBase): Organize data in columns rather than rows, allowing faster queries for large datasets.
• Graph stores (e.g., Neo4j, Amazon Neptune): Designed for handling interconnections between data points.

Strengths

• Handles unstructured, semi-structured, or structured data.
• Scales horizontally, making it great for big data applications.
• Flexible data models that adapt as your data evolves.

Best for

• Real-time analytics.
• Content management systems.
• IoT applications.
• Apps expecting rapid or unpredictable data growth.

Limitations

• Typically, doesn’t offer ACID guarantees, instead prioritizing scalability. (Modern NoSQL systems such as MongoDB, DynamoDB, and Couchbase now provide ACID-like transactional features to meet enterprise needs).
• Querying can be less straightforward, especially across unrelated data.

3. Object-oriented databases

An object-oriented database is a database that stores data in objects, which are defined by their attributes and behavior. This type of database is typically used for complex data structures and relationships, making it well-suited for applications such as e-commerce platforms or financial systems.

Object-oriented databases offer the ability to store both structured and unstructured data, making them suitable for complex data structures. However, they are less widely adopted compared to relational and NoSQL databases and are typically used in niche applications such as CAD systems or engineering projects.

They also support inheritance, where objects inherit characteristics from other objects, allowing for more efficient storage and retrieval of data.

Object-oriented databases (OODBMS) store data as objects, much like programming languages such as Python, Java, or C++.

How they work

Instead of tables, data is stored as objects with fields (attributes) and methods (behaviors), closely aligning with object-oriented programming paradigms.

Popular examples

• db4o.
• ObjectDB.
• GemStone/S.

Strengths

• Seamless integration with object-oriented languages and applications.
• Can store complex data structures directly, reducing the translation between code and storage.

Best for

• Engineering and scientific applications.
• CAD/CAM systems.
• Applications requiring complex data representations.

Limitations

• Less mature than relational or NoSQL databases.
• Not as widely adopted or supported.
• Learning curve for teams accustomed to relational data models.

4. Graph databases

A graph database is a database that is based on graph theory, with nodes and edges representing entities and relationships between them. It stores data in a graph structure rather than in tables, making it ideal for highly interconnected data. Graph databases focus on representing and querying relationships between data points, making them excellent for data with complex interconnections.

These databases are especially useful for social networks, recommendation engines, and fraud detection systems.

How they work

Data is represented as nodes (entities) and edges (relationships) in a graph structure. For example, in a social network, users are nodes and their friendships are edges.

Popular examples

• Neo4j.
• Amazon Neptune.
• ArangoDB.

Strengths

• Brilliant for analyzing and querying interconnected data.
• Extremely flexible and scalable for relationship-centric queries.

Best for

• Social network analysis.
• Fraud detection.
• Recommendation engines.
• Network and IT infrastructure mapping.

Limitations

• Not always the best for highly structured, transactional data.
• Often use specialized query languages like Cypher or Gremlin. (Although many graph databases now support standard query languages such as SQL for broader accessibility.)

Other database types

There are a few additional, specialized database types worth noting:

• Time series databases (e.g., InfluxDB, TimescaleDB): Optimized for timestamped data such as sensor logs, metrics, and monitoring.
• Multimodel databases (e.g., ArangoDB, OrientDB): Support more than one data model in a single backend.
• NewSQL databases: These databases combine the horizontal scalability of NoSQL systems with the transactional consistency (ACID guarantees) and SQL querying capabilities of traditional relational databases. Examples include CockroachDB, Google Spanner, and VoltDB."
• Hierarchical and network databases: Older models, such as IBM’s IMS, built for legacy applications.
• Vector databases: Designed for machine learning and real-time analytics, vector databases specialize in storing and querying high-dimensional vector embeddings. These embeddings are numerical representations of data often used in recommendation systems, semantic search, and computer vision.
• Hybrid solutions: Increasingly common as modern applications get more complex. Multi-model databases support multiple data models within one system. Polyglot persistence uses different database types for specific subsystems. For example, an application might use a relational database for transactions, a NoSQL database for session management, and a graph database for recommendation algorithms.

Choosing the right database

Selecting a database often comes down to understanding your application’s specific needs and constraints.

To make a better decision on what to include in your organization, we've put together some basic factors to compare.

Factors to consider

Here are the factors and some questions to consider:

• Data structure: Is your data highly structured, or does it change frequently?
• Scalability needs: Will your data and user base grow quickly?
• Consistency requirements: Do you need strict transaction accuracy (ACID compliance)?
• Query complexity: Will you run complex queries or analytics?
• Performance: Do you need ultra-fast read/write access?
• Integration: Does your tech stack favor certain database models or systems?
• Operational overhead: Consider maintenance, support, and your team's experience.

(Related reading: database management systems.)

When to use each database

Here's how to tell which database is best for your use case:

• Use a relational database if you require strict data integrity, structured data, and complex relationships (think e-commerce, banking, or ERP).
• Go with NoSQL if your app juggles big, variable, growing, or heterogeneous datasets (like social feeds or IoT platforms).
• Object-oriented databases are best suited for projects tightly integrated with OOP languages needing to store complex, interrelated data.
• Graph databases shine when your data is all about connections and relationships.

Final thoughts

The database landscape is evolving fast, driven by surging data volumes, cloud-native architectures, and new application demands. AI and machine learning are pushing databases to support faster analytics and smarter automation.

So, which database would suit you best?

Well, no single database fits every project. The key to successful development and management is staying informed about the strengths of each database type and selecting the right one for your present and future needs.