Modern Database Types and Examples

Databases are the backbone of data management for businesses today. They store, retrieve, and manage data in various forms and help organizations make informed decisions. But with so many different types of databases out there, it can get a bit confusing knowing which one is right for your organization’s needs. 

Whether you're developing a new application or optimizing an existing system, understanding the different database options available and their strengths can make all the difference in performance, scalability, and data management efficiency.

Types of databases

Consider the various types of databases available today to help you make an informed decision for your organization:

1. Relational databases

A relational database is a type of database that stores data in tables, which are organized into rows and columns. It's designed to manage large amounts of structured data, where relationships between different data entities are important. In a relational database, each table represents a specific type of data, and each row in that table is a record. The beauty of relational databases is that they use Structured Query Language (SQL) to easily manage and retrieve data across these tables, ensuring that everything is neatly organized and accessible when needed.

Pros of relational databases:

• Data is stored in an organized, table-based format, making it easy to access and manage
• Uses SQL, which is a standardized language widely used in the industry
• Supports strong data integrity and accuracy, thanks to built-in constraints

Cons of relational databases:

• Can be complex to scale, especially for large datasets
• Not ideal for handling unstructured or semi-structured data
• Performance can degrade with extremely large volumes of data or complex queries

Examples of relational databases:

• MySQL
• PostgreSQL
• Oracle Database

2. NoSQL databases

NoSQL databases are a class of databases designed to provide flexible and scalable solutions for handling large volumes of unstructured or semi-structured data. Unlike traditional relational databases, which use tables with predefined schemas, NoSQL databases can store data in a variety of formats. This flexibility allows NoSQL databases to efficiently handle diverse data types and easily scale horizontally across multiple servers.

NoSQL databases are particularly useful for applications that require high availability, fault tolerance, and low-latency performance, such as real-time analytics, big data applications, and content management systems. They also accommodate rapidly evolving schemas, making them ideal for projects where data structures may change frequently or unpredictably.

The four main types of NoSQL databases include:

• Key-value stores: Key-value stores are the simplest form of NoSQL databases. They store data as pairs of keys and values, where the key is unique and is used to retrieve the associated value. This makes key-value stores highly efficient for lookups. They are perfect for situations where you need to retrieve a piece of data based on a unique identifier. Examples include Redis and DynamoDB.
• Wide-column stores: Wide-column stores organize data into columns rather than rows, which allows for more efficient querying of large datasets. They are designed to handle very large amounts of data across distributed systems and are commonly used for time-series data or when the schema can vary across different rows. Examples include Apache Cassandra and HBase.
• Graph databases: Graph databases are optimized for storing and querying data that has complex relationships, like social networks or recommendation systems. They represent data as nodes (entities) and edges (relationships), making it easy to navigate and explore interconnected data. Examples include Neo4j and ArangoDB.
• Document databases: Document databases store data in documents, typically using formats like JSON or BSON. Each document is a self-contained unit that can store a variety of data types, making them more flexible than relational databases. Document databases are great for storing semi-structured data where the schema can evolve over time. Examples include MongoDB and CouchDB.

3. Object-oriented databases

Object-oriented databases store data in the form of objects, just like how data is structured in object-oriented programming (OOP). Instead of using tables and rows like relational databases, they allow you to store complex data structures, such as classes and instances, directly in the database. This makes them a natural fit for applications that require the representation of real-world entities, like in software development or simulations. Object-oriented databases aim to bridge the gap between how data is represented in code and how it’s stored in a database, making it easier to work with data in more sophisticated, hierarchical ways.

Pros of object-oriented databases:

• Supports complex data structures like objects, ideal for real-world modeling
• Objects can be reused across applications, promoting data consistency
• Seamless mapping between application code and database, improving development efficiency

Cons of object-oriented databases:

• More difficult to set up and manage compared to relational databases
• Querying can be complex, especially with large datasets
• Less widely adopted, with fewer resources and community support

Examples of object-oriented databases:

• db4o
• ObjectDB
• Versant

4. In-memory databases

In-memory databases store data directly in a computer’s RAM rather than on traditional disk storage, making them incredibly fast when it comes to read and write operations. This means that data can be accessed and processed almost instantly, which is perfect for applications that require real-time performance, like online transaction processing or big data analytics. While they’re not meant for long-term storage, they’re a great option when speed is your top priority, especially in scenarios where the same data is accessed repeatedly in short bursts.

Pros of in-memory databases:

• Extremely fast read and write operations due to data being stored in RAM
• Ideal for real-time applications, such as online transactions or analytics
• Reduces the need for complex disk-based storage solutions, simplifying infrastructure

Cons of in-memory databases:

• Limited by the amount of RAM available, making them unsuitable for storing large volumes of data
• Data can be lost if the system crashes or loses power, since it’s stored in volatile memory
• Can be more expensive due to the high cost of RAM compared to traditional storage

Examples of in-memory databases:

• Redis
• Memcached
• SAP HANA

5. Time-series databases

Time-series databases are optimized for handling time-stamped data, where each piece of data is associated with a specific time. They’re built to efficiently store, query, and analyze data that comes in sequences over time, like sensor readings, stock prices, or server logs. This makes them perfect for tracking data changes over time, especially in applications like IoT, finance, and monitoring systems.

Pros of time-series databases:

• Optimized for handling time-stamped data with high ingestion rates
• Efficient at querying and analyzing trends or patterns over time
• Scalable for high-volume, real-time data collection

Cons of time-series databases:

• Not suited for storing general-purpose, non-time-series data
• Querying can become complex when data spans long periods
• May require specialized skills to fully utilize the database’s capabilities

Examples of time-series databases:

• InfluxDB
• TimescaleDB
• Prometheus

6. NewSQL databases

NewSQL databases aim to combine the scalability and flexibility of NoSQL with the strong consistency and ACID properties of traditional relational databases. They’re designed for high-performance applications that need the reliability of relational models but also require the ability to handle large volumes of transactions or data. NewSQL is perfect when you need both speed and consistency in an enterprise-grade application.

Pros of NewSQL databases:

• Provides ACID compliance for transaction reliability and consistency
• Scalable to handle large amounts of data without sacrificing performance
• Supports SQL queries, making it easy to work with for developers familiar with relational databases

Cons of NewSQL databases:

• May require more resources to scale effectively compared to traditional relational databases
  Not as flexible as NoSQL when it comes to handling unstructured data
• Can be more complex to set up and maintain

Examples of NewSQL databases:

• Google Spanner
• CockroachDB
• NuoDB

7. Vector databases

Vector databases are a relatively new type of database designed specifically for storing and managing vector embeddings, which are high-dimensional representations of data points. These databases are particularly useful for applications in machine learning and AI, where you need to store, search, and compare large volumes of data in the form of vectors, like those used in natural language processing (NLP), image recognition, and recommendation systems. Vector databases excel in tasks that require fast similarity searches, such as finding the closest match to a given vector from a large dataset, making them ideal for AI-driven applications.

Pros of vector databases:

• Optimized for fast similarity searches, making them ideal for AI and machine learning tasks
• Able to handle high-dimensional data, such as text embeddings or image features
• Scalable and efficient at handling large datasets with complex search queries

Cons of vector databases:

• Can be more complex to set up and manage compared to traditional databases
• May require specialized knowledge to effectively use and optimize for performance
• Not as widely adopted as other database types, meaning fewer resources and community support

Examples of vector databases:

• Pinecone
• Weaviate
• Facebook AI Similarity Search (FAISS)

8. Cloud databases

Cloud databases are hosted and managed on cloud platforms, rather than on local servers. They offer the flexibility to scale as needed and are accessible from anywhere, which makes them an ideal choice for businesses looking for low-maintenance, highly available data storage. Cloud databases support both SQL and NoSQL models, allowing organizations to choose the best fit for their specific needs.

Pros of cloud databases:

• Scalable and flexible, with resources that can grow with your needs
• Low-maintenance since the cloud provider handles infrastructure management
• Accessible from anywhere, supporting remote work and distributed teams

Cons of cloud databases:

• Can become costly as data storage and usage grow
• Reliant on internet connectivity, making it vulnerable to downtime
• Security concerns may arise, depending on the cloud provider’s protocols

Examples of cloud databases:

• Amazon RDS
• Google Cloud SQL
• Microsoft Azure SQL Database

9. Legacy database types

Legacy databases refer to older database systems that have been around for decades and were designed to meet specific needs in earlier computing environments. These systems may not offer the scalability and flexibility of modern databases, but they’re still in use today in many legacy systems. 

Two common types of legacy databases are hierarchical and network databases, each with its unique structure and use cases:

• Hierarchical databases: Hierarchical databases store data in a tree-like structure, where each record is connected to a parent, and each parent can have multiple children. This type of structure is useful when you have data with clear, one-to-many relationships, like an organizational chart or product categories. While the hierarchical model is easy to understand and navigate, it can become rigid when trying to handle more complex relationships. Examples include IBM Information Management System (IMS) and Windows Registry.
• Network databases: Network databases are similar to hierarchical databases but allow for more complex relationships by enabling records to have multiple parent-child connections. This model is designed to handle many-to-many relationships, which makes it more flexible than hierarchical databases. However, the complexity of the structure can make it difficult to manage and maintain. Examples include Integrated Data Store (IDS) and Integrated Database Management System (IDMS).

How to choose the right type of database 

Choosing the right type of database for your application is a critical decision that can significantly impact both performance and scalability. With so many options available, it’s important to consider several factors that will align with your specific needs. Whether you’re dealing with structured or unstructured data, or need to scale quickly, understanding your requirements will guide you in selecting the best database. 

Here are some things to keep in mind when making this decision:

• Data structure: Determine whether your data is structured, semi-structured, or unstructured, as this will influence whether you need a relational or NoSQL database.
• Scalability requirements: Think about how much data you expect to handle now and in the future, and choose a database that can grow with your needs.
• Consistency requirements: Some applications require strong consistency, while others can tolerate eventual consistency.
• Performance needs: Consider the speed at which you need to read and write data, and select a database that can meet your performance demands.
• Relationship complexity: Ensure you choose a database that can handle the complexity of relationships between your data.
• Development team expertise: Your database should be one that your team is familiar with or can easily learn to avoid unnecessary learning curves and development delays.

Make databases and data transformation accessible to all users with Prophecy

Prophecy is transforming the way organizations approach data transformation and integration by offering a governed self-service platform. Our mission is to bridge the gap between business and technical teams, allowing for seamless collaboration and eliminating the bottlenecks of data request backlogs. With Prophecy, business users can access and prepare data quickly while ensuring that enterprise governance standards are met, all without relying on technical specialists for every request. By empowering both sides to work together on the same data assets, we eliminate the silos that slow down decision-making and create operational friction.

• AI-powered visual development: Prophecy offers a drag-and-drop interface that makes it easy for business users to build data integrations without coding. It automatically generates production-ready code in Spark and SQL, making data preparation faster and more efficient.
• Embedded governance framework: Our platform embeds quality controls, security policies, and compliance requirements directly into the development process, ensuring that governance is seamless and invisible to users, without slowing down progress.
• Native cloud integration: Prophecy integrates deeply with modern cloud platforms like Databricks, ensuring that your existing infrastructure investments are fully leveraged while providing new collaboration capabilities across your team.
• Collaborative platform: Our real-time collaboration features allow data engineers and business users to work together directly on data assets, eliminating the translation errors and delays that usually plague traditional development workflows.

Learn more about why organizations are making the shift toward cloud-native platforms in our webinar, The future of data engineering: AI, Automation, and Cloud.