Types of Databases Explained

As an IT student or specialist, you can be sure that databases will be a key element of your professional life. So, you’ll need to understand the many types of databases available – what they do, when to use them, and how they’re different. That’s what we’ll cover in this article.

Social media, online banking, e-commerce, AI assistants, and even your mobile phone could not exist without a database! Databases are essential for keeping data accessible and secure in any modern digital product.

The volume of data created and consumed worldwide is constantly growing. We’re now in the zettabyte era, meaning that the world’s total digital data has exceeded a zettabyte – that’s a trillion gigabytes! According to Statista, data volumes will reach 181 zettabytes by 2025!

The demand for data is growing, and new types of databases are gradually showing up. In this article, I will explain the main types of databases – relational, NoSQL, Cloud, and vector  – and their characteristics.

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Types of Databases: The Relational Database

Relational databases are the old reliable, the mother of all databases! Their origin goes back to the 1970s, when an IBM computer scientist named Edgar F. Codd presented the concept of a "relational model" for database management. Codd's revolutionary idea was to structure data into tables linked to each other (relations) that could be easily queried and manipulated using a standardized language. Shortly afterward, this language became known as SQL (Structured Query Language) and relational databases became the main database technology.

How Relational Databases Are Structured

The core component of a relational database is the table; each table consists of rows and columns (something similar to a spreadsheet). This tabular structure allows for the efficient storage, retrieval, and management of data. It also allows us to establish relationships between tables by storing references to foreign tables in a column.

Each table represents a specific entity (e.g. customers, orders, or products). Also known as records, the rows contain the actual data entries in a table. Each row represents a single instance of the entity described by the table: for example, in a table of customers, each row would represent a single customer.

The columns represent the attributes (details) of the data stored in a table. Each column can have a specific data type (such as integer, text, or date). In the table customers, the columns could contain customer IDs, names, emails, and phone numbers.

Working with Relational Databases

It’s much easier to work directly with  relational databases if you know SQL. Structured Query Language (SQL) is the standard language used to interact with relational databases. It allows users to communicate with databases and perform huge and complex queries on their data. Thanks to SQL, five kinds of operations can be performed on relational databases: queries, updates, inserts, deletes, and table management.

SQL queries are used to retrieve specific data from one or more tables. For example, a query can find all customers who made a purchase in the last month. All kinds of filters can be applied to a query: filtering for specific attributes, setting size limits, sorting the output, etc.

Then, there are updates. You can use updates to modify existing data, such as changing the name or address of a customer or the status of an order. Inserts are used to add new rows of data to a table, e.g. adding a new customer to the table customers.

Data that can be inserted into a database and can also be deleted. Deletes are SQL commands that remove data from tables, e.g. deleting outdated records.

Finally, table management operations can be performed to modify the structure of the database itself by creating, altering, and deleting tables, columns, rows, and other database objects.

If you are interested in learning how to write SQL properly, read Tihomir’s awesome article on SQL Syntax.

Uses and Strengths of Relational Databases

One of the main characteristics (and strengths) of relational databases is their ability to provide reliable sequences of operations; these are called transactions. Actually, relational databases use ACID properties (Atomicity, Consistency, Isolation, and Durability) to help guarantee the integrity of the data.

Relational databases are robust and can handle complex queries and transactions; that’s why they’ve become the favorite databases across many industries. In finance, relational databases are preferred for managing vast quantities of transactional data with high reliability. In healthcare, relational databases ensure the consistency and security of patient records. In e-commerce platforms, they manage complex data models that balance product inventories, customer data, and order processing (among other business areas).

Relational databases are also great at protecting and preserving data. Their relational model provides data integrity thanks to primary keys, foreign keys, unique constraints, and other features. This way, the data stored remains accurate and consistent.

The early adoption of relational databases and their constant improvements make them an essential actor in data management and in the IT world in general. If you want to know more about them, I recommend you read Luke Hande’s excellent article What Is an SQL Database?.

Types of Databases: NoSQL Databases

NoSQL databases – also known as non-SQL, Not Only SQL, or non-relational databases – appeared in the early 2000s as a response to the limitations of traditional relational databases.

With the birth of Web 2.0, the whole tech industry changed. New hardware, programming languages, and architecture models showed up. Cloud services started to emerge, and the volume, velocity, and variety of data increased exponentially. Consequently, traditional relational databases struggled to meet the flexibility and large-scale demand of modern applications. This led to the development of NoSQL databases.

NoSQL databases handle unstructured data using a flexible schema; entries in the database can have a different structure. Imagine that you want to let the users of your digital game save crucial information: points, levels, checkpoints, items found, etc. The size and type of data varies for each player, making it a perfect match for a NoSQL database. But there are multiple types of NoSQL databases, as we’ll see.

Types of NoSQL Databases

There are four types of NoSQL databases:

Key-value databases are the simplest type of NoSQL database. In this type of database, data is stored as a collection of key-value pairs. Each key is unique, and its related value can be a string, number, JSON object, or even a binary object. Key-value stores are ideal for caching, session management, and user preferences. The most famous key-value databases are Redis, Amazon DynamoDB, and Riak, among others.

Document databases manage data in document formats – mostly JSON, BSON, and XML. In this context, a document is a unit containing hierarchical data with variable structure and size. Document databases are ideal for applications that require flexible schemas, such as content management systems, blogging platforms, and real-time analytics tools. MongoDB, Apache CouchDB, and Amazon DocumentDB are well-known document databases.

Column-family databases organize data into rows and columns. But unlike relational databases, columns are grouped into families. Each column family can contain an unlimited number of columns, and rows can have different columns. Column-family databases are suitable for analytical, time-series data, and data warehousing applications where read and write operations need to be highly efficient. Examples of column-family databases include Apache Cassandra, Apache HBase, and ScyllaDB.

Graph databases use graph structures with nodes, edges, and properties to represent and store data. Graph databases are perfect for applications with complex relationships and networks, such as social networks, recommendation engines, and fraud detection systems. The most popular graph databases include Neo4j, Amazon Neptune, and OrientDB.

NoSQL Database Use Cases

NoSQL databases are a perfect match for real-time applications that require low latency and high throughput. For instance, online gaming platforms use NoSQL databases to guarantee fast data access and updates for user sessions and leaderboards.

Since they can handle large amounts of unstructured data, NoSQL databases are ideal for Big Data analytics. They can store and process data from multiple sources (such as social media, sensors, and logs) allowing companies to get insights and make data-driven decisions.

Types of Databases: Cloud Databases

Since the early 2000s, Cloud computing has risen exponentially. Cloud services like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform now cover almost every aspect of tech development – including databases. Cloud databases,  or databases running on Cloud computing platforms, have become essential for companies that want to migrate their operations to the Cloud.

Benefits of Cloud Databases

Cloud databases have many benefits, with scalability leading the list. Scalability enables resources to be scaled up or down based on demand. It allows handling peak loads during a particular event (e.g. a viral event, marketing campaign, etc. ) without downtime while still saving costs during lower usage hours. Furthermore, many Cloud databases provide automatic scaling features that adjust resources during slow times (for businesses, this is often on nights or weekends), thus avoiding any manual intervention during peak demand hours.

Cloud databases also provide high flexibility. Cloud platforms support relational, NoSQL, and vector databases, among others. Furthermore, Cloud services allow deploying a given database in various configurations (region, zone) and support backups and replicas.

Cloud databases are also cost-efficient. They operate on a pay-as-you-go pricing model, where businesses only pay for the resources they use. This eliminates the need for huge upfront investments in hardware and infrastructure. Maintenance, patching, and updates are handled by the Cloud service provider, reducing the operational burden and cost for businesses.

Finally, a big advantage of using Cloud databases is accessibility. Cloud providers offer data centers around the world, allowing companies to deploy databases close to their users for lower latency and better performance. Cloud databases can be accessed from anywhere with an Internet connection, facilitating remote work and collaboration for international tech teams.

Types of Databases: Vector Databases

Vector databases are specifically designed to handle high-dimensional data vectors and complex data types (such as images or audio) or any kind of data that can be vectorized. Vector databases are mostly used in the domains of Machine Learning and Artificial Intelligence.

Vector Databases vs. Traditional Databases

Unlike traditional databases, vector databases are used to manage and query vector-based data. This is crucial for AI-driven tasks such as image recognition, Natural Language Processing, and recommendation systems.

Vector databases differ from traditional databases in many ways. The main one is the data structure. Traditional databases typically manage structured data organized in tables with rows and columns; vector databases are designed to handle and store unstructured data in the form of high-dimensional vectors. These vectors often represent embeddings of data, such as images or audio generated by machine learning models.

Another significant difference is the query mechanism. Traditional databases use SQL for querying data; vector databases use nearest-neighbor search algorithms and other vector similarity measures to find and retrieve data that is most similar to a given vector. This is crucial for tasks like semantic search and similarity matching.

Finally, there’s how the databases are optimized. Traditional databases are optimized for CRUD (Create, Read, Update, Delete) operations and ACID (Atomicity, Consistency, Isolation, Durability) compliance to ensure data integrity and reliability. Vector databases are optimized for fast, scalable searches and similarity comparisons across large datasets of high-dimensional vectors.

SQL: A Fundamental Skill Across Database Types

Despite the differences among the different types of databases, one thing remains constant: the importance of SQL!

Originally developed for relational databases, SQL has evolved to become a versatile tool that is also relevant in querying and managing data in various other database systems. If you want to pursue a career in data management or analysis, you need to master SQL.

Some NoSQL databases – such as Amazon DynamoDB and Google Cloud Bigtable – offer SQL-like querying capabilities. These systems allow users to perform familiar operations on NoSQL data structures using SQL. In Amazon DynamoDB, for example, you can use either the DynamoDB API or PartiQL (a SQL-compatible query language) to query an item from a table.

Also, some vector databases provide SQL extensions or SQL-like query capabilities to facilitate interaction with vector data. This allows data scientists and engineers to use familiar SQL commands for managing and querying high-dimensional data vectors.

However you slice the data, SQL is widely used. According to the Stack Overflow Developer Survey 2023, professional developers are more likely to use SQL than other database technologies: out of 2023’s 10 most-used database technologies, 6 are relational databases.

Source: Stack Overflow Developer Survey 2023

Despite the diversity of database technologies, SQL remains a fundamental skill for anyone working in data management or analysis. Mastering SQL also opens up a wide range of career opportunities in the data-driven world. As databases continue to play an essential role in technology, SQL will remain a vital tool that allows experts to perform operations on their data.

If I have not convinced you yet about the relevance of learning SQL, Jill Thornhill will do it in her great article The Future of SQL.

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