Database Types: A Complete Guide to Choosing the Right One

There isn't complete agreement on database types. Databases can be classified based on data model, workload, and architecture. Some of the main types of databases are based on relational and NoSQL data models, as well as specialized systems for time-series data, vector search, distributed SQL, analytics, and other workloads. The correct type of database will be dependent on the structure of your data, how your data is going to be queried by the application, consistency requirements, how transactions will be handled, and how the system will need to scale.

1. When selecting a database for a business creating a website, SaaS product, Mobile App Development, e-commerce solution, CRM, enterprise solution, or other system, your database selection can impact performance, scalability, and development costs. This is where your partnership with Xcentric Services can assist in not just database selection, but architecture selection as well

Main Types of Databases - An Overview

The overview will help you understand the main types of databases, but this is not an exclusive list, and there will be some overlap.

How Database Types Are Classified

There is a tendency in novice research into database classification to assume all categories reside on the same level. They do not. Classification along three axes at least is a valid approach.

Data modeling relates to how information is represented and structurally organized. In relational databases, data is structured in tables with relations. In contrast, NoSQL systems employ document, key-value, wide column, or graph data models.

Performance and architecture impact workload. Transaction processing, analytical processing, time series workloads, search, and vector similarity workloads have different performance and architecture implications.

Architectural variations include scale and distribution, and multi-model databases. Special-purpose data stores employ architectural variations that go beyond table variations.

Different database classifications describe different properties. Therefore, a single database can be relational and distributed, relational and analytical, or can even support multiple data models. This does not mean that we have too many types of databases. In the absence of clear and strict classification, this is the reason we do not have a definitive number of database types.

We should distinguish between the types of databases and the products that implement certain database models. PostgreSQL, MySQL, and MongoDB are just a few examples of database models and products. PostgreSQL is a relational database, while MongoDB is a document-oriented database.

1. Relational (SQL) Databases

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Relational databases are some of the oldest and most prominent databases. Relational databases organize data into tables and store that data into rows and columns, with relationships expressed through keys.

Relational databases primarily use SQL as a query language and best support applications requiring data relationships and transactions, all implemented within a structured query.

Common relational database products include PostgreSQL, MySQL, Microsoft SQL Server, and Oracle Database.

Relational databases are a good first choice for applications that contain structured data with defined relationships, e.g. customers, orders, products, invoices and payments.

Supabase uses PostgreSQL as its base database. Thus, Supabase is an example of a PostgreSQL-based application platform and not a different type of database. When comparing relational and non-relational databases in detail, hyperspecific comparison of SQL and NoSQL should take over instead of having to repeat the entire framework.

The NoSQL family includes several different approaches, rather than a single model, and is therefore described as NoSQL databases. The most common models of NoSQL databases are document, key-value, wide-column and graph databases. The appropriate model depends on how the application's data is accessed.

1. Document Databases

Document databases are a good choice as records are stored as documents and become particularly useful when application data is naturally nested or when fields in application data need to vary and become more flexible.

MongoDB is a succesfully implemented document database. Records in Mongo DB are stored as BSON documents and can contain nested documents and arrays.

Document databases are used to build content platforms, catalogs, and applications where the data is structured in a way that maps naturally to the objects in application code.

Firestore is another service that offers a document-oriented database, however, Firebase should not be grouped with the other database types.

1. Key-Value Stores

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Redis supports a variety of native data structures such as strings, hashes, lists, sets, sorted sets, streams, and more. It is frequently referred to as an in-memory data store, but calling a system “in-memory” is more of a statement about the storage and performance characteristics of the system than of the data model. As such, a system can be in-memory and key-value.

Another example that fits well is DynamoDB. It supports both key-value and document data models.

1. Wide-Column Databases

Wide column databases store data in column families and are intended for workloads that have large-scale writes and a lot of horizontal scaling.

Apache Cassandra and Apache HBase are good representative examples and can be quite useful for applications that deal with a large amount of distributed data where access patterns are mostly known.

1. Graph Databases

Graph databases are built for systems that deal with lots of relationships. A lot of systems, especially those that deal with lots of records, must maintain lots of connections. Graph databases deal with this by treating records as nodes and connections as edges.

Neo4j is a good example of a graph database. Graph databases can be quite useful for recommendations, fraud detection, social networks, etc, where relationships and connections are as important in the system as the records themselves.

1. Time-Series Databases

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Time-series Databases are based on time-associated data. Examples of these Databases are InfluxDB and TimescaleDB. Examples of Data that these Databases process include application metrics and data from IoT devices, financial data, sensor data and operational data.

Time-series workloads include scenarios where services are continuously writing data points to the Database and subsequently asking questions like:

• What were the performance metrics of the system over the last 24 hours?

• What was the temperature in the room from 11:00 AM to 2:00 PM?

• What metrics exceeded the threshold?

• What was the trend of application usage over time?

Time-series Databases can optimize such workloads by employing custom storage, indexing and querying techniques.

1. Vector Databases and Vector Extensions

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Vector databases will be essential for AI and semantic search applications as well as recommender systems and retrieval-augmented creativity.

However, there is an important distinction in the classification and scope of vector databases: vector search is a capability or a workload pattern that exists alongside relational, document, and graph databases. Vector systems contain embeddings and allow for similarity searches, and vector databases, such as Qdrant, are used to store and facilitate fast and efficient searches of high-dimensional vectors for use cases like semantic search or recommendation systems.

Examples of dedicated vector databases include Pinecone, Qdrant, Weaviate, and Milvus, which are all used primarily for AI search and retrieval. Jointly with PostgreSQL providing a relational model, pgvector, provided as an extension by PostgreSQL, is an example of a vector database extension. Some of the modern multi-model distributed databases provide support for a variety of operational and AI workloads.

Qdrant is an example of a vector database that also stores pertinent payloads and metadata and performs similarity search. Weaviate also calls itself an open-source AI vector database. Milvus is yet another open-source vector database designed for vector search at scale.

PostgreSQL + pgvector is a major differentiator for developers. pgvector is an extension for PostgreSQL and is therefore not a vector database in itself.

1. Distributed SQL (previously known as NewSQL)

Distributed SQL databases combine relational systems and SQL interfaces with a distributed architecture. The purpose is to build a relational system that can distribute data and workload across several nodes to support large-scale transactional workloads.

Some examples are CockroachDB, Google Cloud Spanner, TiDB and YugabyteDB. Current vendor documentation describes YugabyteDB as a distributed SQL database with PostgreSQL compatibility, horizontal scalability and geo-distribution.

According to Google, Spanner is a distributed database with relational capabilities, SQL support and strong transactional semantics. Similarly, CockroachDB documentation cites distributed transactions and the ACID property for its distributed deployment.

Distributed SQL is an attractive choice for an application that offers:

• Horizontal scaling

• High availability

• Multi-region compute deployments

• Relational data and SQL

• Transactional workloads

This, however, can introduce architectural and operational challenges for a small application.

1. Search Engines and Search-Oriented Data Stores

In the context of database architectures, Elasticsearch and OpenSearch tend to be encountered less often. However, they should not be classified as traditional databases.

Instead, they should fall under the category of search-oriented data stores and retrieval systems, adept at supporting full-text search, indexing, filtering, and other types of searches across large data sets.

In this type of architecture, PostgreSQL is commonly used as the system of record, while Elasticsearch or OpenSearch is used to provide specialized search functionality.

It is essential to make this distinction because, unlike a search engine, using one does not necessarily mean replacing an application’s primary database.

1. Multi-Model Databases

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Multi-model databases are designed to support more than one data model within a single system. ArangoDB and Azure Cosmos DB can be classified as examples of multi-model databases. Azure Cosmos DB is described by Microsoft as a globally distributed multi-model database service that supports numerous NoSQL, MongoDB, Cassandra, Gremlin and Table APIs.

Supports for multiple models does not mean that every model will be supported equally for every workload. Each workload should drive design.

1. Analytical (OLAP) vs. Transactional (OLTP) Databases

OLTP and OLAP describe workload and system-design priorities. They are not competing database models.

OLTP (Online Transaction Processing) focuses on systems involved in Order Entry, Account Updates, and Payment Processing transactions, while OLAP (Online Analytical Processing) focuses on analyzing large data volumes. Transactions, Concurrency, and Application Responsiveness are emphasized in OLTP. Analytical workloads comprise aggregations, reporting, dashboards, and BI (Business Intelligence) and are emphasized in OLAP.

A relational database is capable of supporting either OLTP, OLAP, or both, based on a database architecture and workload.

Some public Examples of Analytical Data Warehouse Platforms are: Snowflake, Google BigQuery, Amazon Redshift. These should not be considered Google, Amazon, etc., simply Other DB Models.

How to Choose the Right Database Type

When selecting a database, one should consider the application first, rather than the brand of the database.

• If your application consists of a lot of transactions, workflows, and relations, it is best to look into relational databases.

• If your data is more naturally documented in flexible formats, you might want to consider a document database.

• If you want fast lookups, caching, and storage of sessions in your application, key-value databases are also a choice.

• If you want to house continuously recorded, timestamped metrics, find a time series database.

• If your application deals with semantic search, recommendations, or AI retrieval, consider looking into vector search engines either through a specialty vector search engine platform or through a database in which vector search engines can be added as an extension.

• For applications dealing with large transactional workloads spanning multiple regional servers through a distributed relational database architecture, look into distributed SQL.

What are the Most Important Aspects of Choosing a Database

The most important aspect when choosing an option is what the requirements are:

• Data structures

• Query patterns

• Transactions

• Scalability

• Availability

• Geographic distribution

• Expertise

• Complexity

• Budget

• Cloud

• Integration with the rest of your stack

For a more in-depth relational versus non-relational discussion, the SQL vs. NoSQL article will provide that information instead of covering the same information here

Xcentric Services can assess application requirements and choose an architectural structure if you are designing a SaaS platform, an e-commerce system, mobile applications, or enterprise products. Xcentric works on designing custom web applications as well as providing application development and

business software solutions.

Why Database Architecture Matters in Your Business and How Xcentric Services Can Help You Achieve It

Choosing a database is not purely a developer’s decision. Application scalability and security, performance, and maintenance over the long term depend on database architecture. It is important to consider database architecture along with the rest of the backend architecture.

Xcentric's web development work revolves around designing custom web applications, SaaS solutions, and enterprise portals. Its development services cover strategy, architecture, development, testing, and QA releases.

For organizations that require more engineering know-how, Xcentric offers SQL database developers as part of its staff augmentation service. These developers can create and optimize database schemas. Xcentric Services evaluates the data model and data-intensive workloads of an application across the scalability and technology stack dimensions before suggesting a database platform for an application.

Conclusion

Choosing the right database isn't about having the most complex systems. It's finding the appropriate balance for your product, users, data and the long-term vision you have for your business.

The best approach is to try to choose the simplest database architecture that will meet the

needs of the business today and allow for future growth.

For businesses needing help getting from technical requirements to a scalable digital product, Xcentric Services offers strategic design and development services using the technologies your product needs to get the job done, all grounded in a clear understanding of your business needs.