The term NoSQL was originally intended to describe databases that do not use Structured Query Language. However, that term has evolved since its first use back in the 90s. In this article, Alex Williams explores key benefits of NoSQL databases and when to use them.
The term NoSQL was originally intended to describe databases that do not use Structured Query Language. However, that term has evolved since its first use back in the 90s. Now, the phrase “not only SQL” accurately describes NoSQL databases. NoSQL databases are also commonly referred to as non-relational databases, as opposed to SQL databases’ other name, relational databases.
Key Benefits of NoSQL Databases
Developers familiar with SQL databases know about schemas all too well. When it comes to NoSQL databases, no schemas are used. That’s because these non-relational databases are meant to be modified as one pleases. No structure needs to be followed, and there are no patterns by which data must be organized. In a sense, NoSQL databases are free from the chains that SQL databases impose on their users. Thus, they significantly improve DevOps.
NoSQL databases have developed their popularity largely thanks to the flexibility they offer. Developers who want to create a database quickly, with very little upfront planning, can easily do so with NoSQL databases. There is no need to create a database architecture where tables and schemas are predefined.
Rather, developers can start their NoSQL databases with a single document that resembles a JSON object. As their project grows, they can create new documents that vary greatly in the type of data they store, how much data they store, and what the actual data looks like. Developers do not need to create new tables as their project grows in complexity. They do not need to update existing tables, add new secondary keys to existing tables, and so on. NoSQL databases are easy to develop, flexible, and perfect for those who want a database that can be modified on the go.
Built for Unstructured Data
NoSQL databases were designed with projects in mind that have unstructured information; data that is not highly dependent on relations, hence the term non-relational database. NoSQL databases can manage large amounts of data that have little to no structure thanks to their schema-free nature. Although there are no tables and schemas, links between data can still be made. This aspect of NoSQL databases makes them perfect for Machine Learning and Big Data.
When it comes to large applications, businesses and developers must be aware of scalability. Having a plan in place for scaling your database is fundamental to your project’s success. While traditional SQL databases scale vertically, NoSQL does the opposite—it scales horizontally.
Horizontal scaling is an extremely attractive feature of NoSQL databases. When your project needs more capacity for data, all you need to do is add additional nodes—computers— to your system. This is significantly easier than vertical scaling, where databases need better hardware added to their collective system. What’s more, as most NoSQL database providers are cloud providers, they handle the scaling for you. Plus, many of these providers allow your database to use auto-scaling.
Essentially, when your application needs to increase data storage and access capacity, your database will add nodes in real-time. If there was a surge in users, when that surge dies down, your database will drop nodes.
As most developers have a solid grasp of SQL, they understand the learning curve associated with it. On the other hand, those who have worked with NoSQL databases know that the learning curve is quite minimal. NoSQL databases are quick and easy to get running. Storing and retrieving is even easier.
Many popular NoSQL databases store data in what’s essentially a JSON object. That means virtually all developers will already know how to visualize, extract, and add data to these data stores.
Additionally, many NoSQL database providers are known for having excellent documentation. The developers of such databases intended their products to be easy for newcomers to get a database up and running. Similarly, there is a consistently growing community of NoSQL users on the internet to help.
Types of NoSQL Databases
Document databases, such as Mongo DB, are the types of NoSQL databases that use what’s basically a JSON object for data storage. Inside of these objects, users can store anything they want—arrays, other objects, strings, values, and Booleans. One of the most attractive aspects of document databases is that users can store documents inside of other documents.
Plus, as standard with NoSQL as a whole, document databases can be modified on the go. That means that developers can change the composition, structure, and types of data being stored. No schema needs to be followed.
Someone who’s managing a document database may have some documents that hold a website user’s name and phone number. That same document may also hold a different user who has a name, phone number, email, profile picture, and an “about me” paragraph.
Graph databases differ greatly from document databases. Think of a graph database as a web of information where different strings of information meet at various points in the web. Graph databases use what are called nodes and edges—nodes are the points in the web that are connected through edges.
These nodes hold information fundamental to the project. When two nodes have information that overlaps, an edge is formed between the two. Each node can have as many edges as it wants or needs.
These types of NoSQL databases are perfect for social media applications where there are numerous users who all hold shared data.
Column-orientated databases are the most similar to SQL databases in terms of structure. If you were to visualize a NoSQL column database, you’d picture a table. However, this table, and these databases, are much more flexible than the tables that are found in SQL databases.
Column databases hold key data in rows. One item can be found in a row that may have several different columns where related data is stored. Each row may have a different number of columns with different types of data.
These databases are known to be used in Big Data processing applications as they’re low-cost, fast, and reliable.
Key-value NoSQL databases are straightforward for users to understand. A piece of data—the value—is stored inside the database that is associated with its key. This makes finding the data easy, as all you need to do is query for the key.
Key-value pairs are ideal for scenarios where large amounts of simple data can be stored. The values being stored can be virtually anything—it could be a string, an object, or a number.
Key-value databases are used frequently to store temporary and basic information on projects where another type of database is being used. For example, a project that has a large number of users may store users’ preferences for various settings in a key-value database.
When to Use NoSQL
NoSQL databases are powerful, flexible, and easy to use. This makes them wonderful choices for virtually any project. While they may not always be used alone, they can certainly pair well with other NoSQL databases or with SQL databases.
To know when to use a NoSQL database, ask the following questions about your projects’ needs:
- Do you have large amounts of unstructured data?
- Do you want to be able to modify your database stores on the go as the project develops?
- Are you worried about scalability?
- Do you want your project to get up and running quickly, with little database planning?
- Are you looking for a cheap data storage option?
- Do you want your data stored on the cloud?
If you answered yes to any of the above questions, you should consider using a NoSQL database. With several options in providers and types of databases, you’ll easily find something to meet all your needs. What’s more, getting started will be pain-free.
The Bottom Line
NoSQL databases are hot topics in the technology industry right now. For good reason, NoSQL databases have seen rapid and consistent growth in the number of users. They’re reliable, flexible, ideal for large amounts of data, and easy to get started with