What is non relational database

Let’s start simple. What is a database? A database is a set of data held in a computer that can be accessible in various manners. There are two main database types: Relational & Non-Relational. So, what’s the difference?

A relational database is structured, meaning the data is organized in tables. Many times, the data within these tables have relationships with one another, or dependencies. A non relational database is document-oriented, meaning, all information gets stored in more of a laundry list order. Within a single construct, or document, you will have all of your data listed out.

SQL Databases (Relational)

SQL is short for Structured Query Language, basically meaning a very firm way of sorting through data in the form of tables, columns, and rows. How is data structured in an SQL database? The table itself would be made up really of one variable or object that we would be looking through. The column would represent the data point itself that needs to be stored and the row is a record of the data points per column.

For example, if you are looking to sort data regarding what the weather is at a certain time of the day during a certain day, it would be structured as the following:

• Table: Weather
• Columns: Days of the Week
• Rows: Time of Day
• Data Points: Degrees Fahrenheit

In this structure, all queries would be related to this table and the structure of the table would allow for easy sorting, filtering, computations, etc. If we ever need to establish a connection between tables, say, you want to know what the weather was at a certain time and relate that to a baseball game’s predicted score, then what we do is create what is called a key. This key allows for connections to be made between two or more tables to solidify associations between the two.

Some popular SQL database systems include:

• Oracle
• Microsoft SQL Server
• PostgreSQL
• MySQL
• MariaDB

NoSQL Databases (Non-Relational)

In contrast to a relational database, a NoSQL database is one that is less structured/confined in format, and thus, allows for more flexibility and adaptability. If you are going to be dealing with a dataset that isn’t clearly defined, meaning not organized or structured, you likely won’t have the luxury of establishing defined tables and relationships amongst the dataset.

For example, Facebook Messenger uses a NoSQL database, because the information that is being gathered isn’t structured enough to be segmented into tables and define relationships between each other. With tons of unstructured information, it needs to be held in a non-relational database. Think of the information as being stored on one large word document. Everything is there. As more information gets entered, the document gets longer. If you want to find and pull data, you have to in essence ‘control/command + F’ and search for the data itself.

Some popular NoSQL databases include:

• MongoDB
• Google Cloud Firestore
• Cassandra
• Redis
• Apache HBase
• Amazon DynamoDB

Final Showdown: Pros and Cons of Relational and Non-Relational Databases

Now we answer the question you’re really looking for. Which type of database should you use? Well, there are some questions you should ask yourself that are outlined below. If you answer yes to the relational questions, then use a SQL database. If you answer yes to the non-relational questions, then use a NoSQL database.

Pros of a Relational Database

1. Data is easily structured into categories.
2. Your data is consistent in input, meaning, and easy to navigate.
3. Relationships can be easily defined between data points.

Pros of a Non-Relational Database

1. Data is not confined to a structured group.
2. You can perform functions that allow for greater flexibility.
3. Your data and analysis can be more dynamic and allow for more variant inputs.

Think you’ve got it? Great! Need some help? Reach out to  and we’ll make sure you’re making the right decision, free of charge 😉 

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The views expressed in this post are the writer's and do not necessarily reflect the views of Aloa or AloaLabs, LLC.

Imagine your data is a dog. In front of it, you place an Excel sheet and a Word doc. Which one will the dog go to?

It may be a little silly – but it’s a good way to understand exactly what kind of data works for the two main types of databases – relational and non-relational. Let’s go over the difference between these two types of databases – as well as list some key questions every business should answer before choosing a database.

Relational Databases
Let’s go back to your “data dog.” Maybe it prefers the Excel sheet. Why? Because it fits nicely into rows and columns.

A relational database is one that stores data in tables. The relationship between each data point is clear and searching through those relationships is relatively easy. The relationship between tables and field types is called a schema. For relational databases, the schema must be clearly defined. Let’s look at an example:

Here we see three tables all providing unique information on a specific dog. A relational database user can then obtain a view of the database to fit their needs. For example, I might want to view or report on all dogs over 100 pounds. Or you may wish to see which breeds eat dry food. Relational databases make answering questions like these relatively easy.

Relational databases are also called SQL databases. SQL stands for Structured Query Language and it’s the language relational databases are written in. SQL is used to execute queries, retrieve data, and edit data by updating, deleting, or creating new records.

Early adoption and widespread use keep SQL databases a popular data management system. This is in part due to the lack of training required for workers as many data scientists learn SQL early on.

Popular Relational/SQL Databases

SQL Server

SQL Server is a relational database management system developed Microsoft. They offer multiple editions with varying features to target different users.

Pros: SQL Server boasts a rich user interface and can handle large quantities of data.

Cons: It can be expensive – with the Enterprise level costing thousands of dollars.

MySQL

Pros: It’s free and open-source. There’s also a lot of documentation and online support.

Cons: It doesn’t scale very well. MySQL tends to stop working when it’s given too many operations at a given time.

PostgreSQL

Pros: Like we said, extensible. If you need additional features in PostgreSQL, you can add it yourself – a difficult task in most databases.

Cons: For beginners, installation and configuration can be difficult. There’s also not nearly as much documentation as more popular databases like MySQL.

Non-Relational Databases

Back to your “data dog.” This time, it went over to the Word doc. Why? All the open space! The data comes in all different shapes and sizes – it needs room to spread out.

A non-relational database is any database that does not use the tabular schema of rows and columns like in relational databases. Rather, its storage model is optimized for the type of data it’s storing.

Non-relational databases are also known as NoSQL databases which stands for “Not Only SQL.” Where relational databases only use SQL, non-relational databases can use other types of query language.

There are four different types of NoSQL databases.

1. Document-oriented databases – Also known as a document store, this database is designed for storing, retrieving and managing document-oriented information. Document databases usually pair each key with a complex data structure (called a document).
2. Key-Value Stores – This is a database that uses different keys where each key is associated with only one value in a collection. Think of it as a dictionary. This is one of the simplest database types among NoSQL databases.
3. Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table.
4. Graph Stores – A graph database uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.

Non-relational databases are becoming more popular as more and more businesses begin to leverage big data for analysis and reporting. Since critical data doesn’t always fit well into a pre-defined schema, NoSQL databases allow more flexibility.

Popular Non-Relational/NoSQL Databases

MongoDB

MongoDB is a document store and currently the most popular NoSQL database engine in use. It uses JSON-like documents to store data and is run over multiple servers. MongoDB allows for auto-sharding which is a type of database partitioning that separates very large databases into smaller, faster, more easily managed parts called data shards.

Pros: MongoDB is very easy to setup and provides a lot of professional support.

Cons: They don’t allow joins. Joins are used to combine data or rows from two or more tables based on a common field between them. MongoDB does have a  LOOKUP function but tells its users not to rely on them.

Redis

Redis – Remote Dictionary Server – is a key-value store. It supports different kinds of abstract data structures such as strings, lists, maps, sets, sorted sets, and more. It’s also open-source.

Pros: It supports a large variety of data types and is easy to install.

Cons: Like MongoDB, it doesn’t support joins. It also requires knowledge of Lua, a high-level programming language.

Relational vs Non-Relational Databases

To summarize the difference between the relational and non-relational databases: relational databases store data in rows and columns like a spreadsheet while non-relational databases store data don’t, using a storage model (one of four) that is best suited for the type of data it’s storing.

Questions to Answer Before Choosing a Database

What type of data will you be analyzing?

Does your data fit comfortably in rows and columns? Or is it better suited in a more flexible space? The answer will tell you whether you need a relational or non-relational database.

How much data are you dealing with?

A good rule of thumb is this – the bigger the data set, the more likely a non-relational database is a better fit. Non-relational databases can store unlimited sets of data with any type and have the flexibility to change the data type.

But relational databases work best when performing intensive read/write operations on small- or medium-sized data sets.

What kind of resources can you devote to the setup and maintenance of your database?

Here’s another good rule of thumb – the smaller your engineering team, the more likely a relational database is a better fit. Why? For one, relational databases take less time to manage. Also, SQL is a more well-known query language. It’s more likely your team already knows it.

Non-relational databases may require more programming knowledge – meaning your team may have to learn other types of query languages. Or you’ll need to hire someone with a code-heavier background.

Do you need real-time data?

There’s a serious buzz around real-time analytics. The competitive edge it brings and its impact on decision-making cannot be understated. However, it’s important to note that not every organization needs real-time data. Maybe your data doesn’t change that much. Maybe you’re more interested in analyzing past data sets. In that case, relational databases work well.

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