How to Choose the Right Database for Storing Your Data

Choosing the correct database for your projects is no easy decision. After all, picking one is a long-term commitment, and realizing you’ve made the wrong choice may force you to undertake a risky and costly procedure. In this article, we’ll discuss the types of databases there are, their advantages and disadvantages, and other topical points to help you decide what’s the right choice for you.

Although, before we begin, let’s define some general terms you should know before we start overviewing the topic. The most important ones are relational and non-relational databases.

Non-relational databases

Sometimes referred to as a NoSQL (Not Only SQL) database, it is a type of database management system. A non-relational database's primary distinguishing characteristic is that its data organization is not based on the conventional relational model. These databases have flexible schemas and are built to manage enormous volumes of both structured and unstructured data. Some core use cases for non-relational databases include applications where speed, scalability, and high performance are key. Also, check our article if you’d like to read more on structured vs unstructured data.

Relational databases

Unlike non-relational, relational databases organize data into tables consisting of rows and columns. The tables are related, resulting in data units having a fixed place. Therefore, the benefit of using relational databases is that they provide a simple and clear-cut structure that can be more easily dealt with by developers. Relational databases are also queried using structured query language (SQL), so they are often referred to as SQL databases.

SQL and NoSQL databases 

While in the above section, we mentioned that relational databases are often referred to as SQL databases, they aren’t the same. SQL is a programming language built for managing and manipulating relational databases. Notably, it provides a standardized way to interact with relational databases, allowing you to define, control, and query the stored data. However, it itself isn't a database.

On the other hand, NoSQL and non-relational databases are the same thing. NoSQL simply stands for “Not Only SQL,” suggesting that these databases do not exclusively use traditional SQL for data management. What makes NoSQL potentially attractive for software engineers is that it delivers where SQL fails. NoSQL can be the correct choice when you must handle unstructured or semi-structured data quickly and on a large scale. Crucially, though, you miss out on the convenient and standardized way of querying with SQL by choosing NoSQL.

Types of NoSQL databases 

Something else you should consider when choosing NoSQL is the various types of databases it contains, starting with key-value.

Key-value database

Commonly seen as the simplest type of NoSQL database, key-value databases have data sets with no predefined structure, resulting in exceptionally flexible data formats. The foundation of this database lies in its approach to storing data as a collection of key-value pairs. Each data value gets assigned its designed key, and upon requiring a specific key, said data value is retrieved. 

Because, comparably, it's so simple, key-value databases become highly resilient to failure, easy to scale, quick, and crucially capable of handling high loads. They also excel at simple read-and-write operations, thus cases where high-performance data retrieval and low-latency access are where these databases excel.

Overall, the features of key-value databases make them ideally suited for systems requiring quick data access and simple querying. Caching, session management, user profiles, real-time analytics, and high-scale distributed systems are just some of the examples where it excels at.

Column-Oriented databases

Instead of rows, this type of database focuses on columns. Curiously though, the structure of this database isn’t that much different from row-oriented databases. One of the primary aspects of column-oriented databases is how a single cell of a table row contains multiple types of data about a singular object. Imagine you want to easily retrieve information on an employee working for your firm. By requesting the appropriate row, you’ll get a multitude of columns, each providing different information like age, position, name, etc.

The convenience of having such a database is that it allows you to find things by their categories. For example, going back to our imaginary employee, let’s say you want to see all the people in the firm who have the same position as the said employee. By using a column-oriented database, a single request will retrieve all the results, whereas, with a row-oriented database, you’d have to scan row by row to find the necessary information. As such, this type of database is excellent at dealing with big data and real-time analytics.

Document-oriented databases 

As the name suggests, data is stored within documents in these databases, usually in JSON or BSON (binary JSON) formats. Each document can have a unique structure and completely independent types of fields. Notably, due to the documents' independence, they don’t require a predefined schema. 

Because this database allows for documents of varying types to be collected into buckets (essentially groups where similar documents are housed together) and uses a schema-less approach, it is considered highly flexible. You can update, change or remove different attributes without worrying about ruining your schema.

Document-oriented databases are usually chosen by software engineers when scalability and flexibility are needed in areas like content management solutions, e-commerce platforms, and collaborative applications.

Graph databases

The basis for a graph database is that the data is modeled as nodes connected by edges. The nodes represent data entities, for example, objects or people, while the edges show their relationships. Where such a database shines is in graph traversal. Finding related information becomes straightforward since the relationships between nodes can be easily followed.

What sets graph databases apart is how effectively they avoid multiple indexing and referencing as the connections between numerous relationships are already reflected. Because of these features, graph databases are becoming widely used in many industries, from data intelligence and fraud detection to AI and Machine Learning.

SQL’s ACID properties

Having examined the various types of NoSQL databases and their unique features, let’s further delve into what makes SQL different, and to do so, we needn't look further than the ACID (Atomicity, Consistency, Isolation, and Durability) properties.

Before delving into each feature separately, note that these 4 properties define a transaction. Therefore, if a database operation has these properties, it can be considered an ACID transaction, and the data storage systems that apply the operations are thus considered transactional systems.

Now, let’s examine the ACID properties one by one. Atomicity means that each statement in a transaction is treated as a single, indivisible unit, for example, deleting, writing, or updating data. This means that either all the changes are executed or none of them. The benefit of Atomicity is it helps prevent data loss and corruption from happening as partial updates (which can lead to data corruption) are avoided.

Consistency ensures that transactions bring the database from one consistent state to another. Therefore the changes to tables are done in predefined, predictable ways. With Consistency, errors in your data don’t affect the overall integrity of your table.

Isolation is done to avoid concurrent transactions interfering with each other. Each request occurs individually, meaning even if multiple users are reading or adjusting the table in any way, non-repeatable and phantom reads can't happen.

Durability is arguably the most straightforward aspect of ACID as it simply guarantees that changes to your data upon successfully executed transactions are saved so that even if a failure like a crash or power outage occurs, no data gets lost.

SQL vs NoSQL databases