When building a new application, choosing between a relational database and a non-relational one is among the first important decisions you’ll have to make. According to DB-Engines, relational databases still dominate the database market as of 2022 with 6 relational database management systems making it to the top 10. Oracle, MySQL, Microsoft SQL server, and PostgreSQL go in the top 4, followed by NoSQL databases MongoDB, Redis, and ElasticSearch.
But picking between RDBMS vs NoSQL isn’t black or white―each technology has trade-offs that need to be carefully examined along your own business requirements. This is what this article discusses.
What’s RDBMS Vs NoSQL
A relational database management system (RDBMS) is a software that interacts with data stored in a SQL database like MySQL, PostgreSQL, or sqlite. In a relational database, data is structured into tables with rows and columns that sometimes reference each other, like spreadsheets with superpowers linked together. You use a structured query language (SQL) to perform actions against the database, like reading or updating data.
NoSQL is another type of database that doesn’t impose a given structure on the data it contains. Instead, data is organized into collections of documents that have no formal relationships. You still use a query language to manage the data, but the syntax is flexible.
Why RDBMS Vs NoSQL
Different database paradigms means different pros & cons, and understanding each one will allow you to pick the technology that will serve your business best. Picking a database isn’t an end in itself. It is actually commonplace for companies to use several types of database depending on their needs.
Choosing the right database not only saves costs, but also increases the end-user experience. There are things you can’t do efficiently with one or another which results in extra efforts being needed, and having the wrong approach shows in an user interface with long load times.
Each technology requires different skills and tools, so you’ll need to hire different people and third-party services to grow your app. If you choose a database and decide later it doesn’t fit your needs, you’ll have to train your developers―or fire them if you can’t afford it.
Main Differences Between Relational Vs NoSQL
- Complex queries with SQL - SQL packs many features allowing it to create complex queries from the database layer. Joins for example, allow to retrieve rows from different tables and compute aggregate values from them. Say you have a table containing users and another containing blog posts. With SQL, you can list down all the blog posts from every user starting from the most recent one―while taking into account their timezone―in a single SQL query! Doing this in NoSQL would require several database lookups. This is a big deal to increase performance, because data processing using database engines is much faster than querying the data, sending it over a wire, and having your program crunch the numbers. Because of this, SQL is great when you need to work with related data.
- Optimized table-like structure - Relational databases are like spreadsheet tables with rows and columns, linked together and optimized for fast operations. When you create a table, you define its data structure and the engine will optimize it. You can add columns later, but all the data entries must follow the designed structure. Having predictable data format is a good thing because that’s what allows advanced optimizations.
- Data quality and security - SQL databases use separate tables designed to avoid repeating the same information. This property makes them more reliable because they offer a single source of truth―no data is scattered across different places. On the other hand, built-in data type validation also provides some level of security regarding what's put inside the database.
- Update performance - Since a piece of data is always in a single place, it only takes a single command to update it in a consistent way across the entire database. With NoSQL, you would need to find the data and update it each time it’s used―and when you forget to change an occurrence, bugs happen!
- Cost-efficient - Relational databases have been around forever, which makes them cheaper to operate. Tools and resources are widespread. And the storage efficiency of RDBMS makes hosting less expensive.
- Lack of flexibility - SQL requires data in a given table to follow the same structure as it was originally designed. Changing this structure without causing data loss is not straightforward: you need to back up the data each time in case something bad happens, change all the related queries to follow the new structure, and perform the migration itself with dedicated tools.
- Not suited to big data - Relational databases aren't good at handling large or complex datasets. You can always throw more CPU at your servers, but it’s not cheap and the laws of physic will limit you eventually. Foreign keys also limit your parallel processing capabilities because you need to add referenced data first, making it slower.
- Learning curve - For beginners, RDBMS seem a bit daunting. Learning how to properly design a database structure is not straightforward either and takes months of practice.
- Flexibility - A NoSQL database allows documents to be created without defining a structure first: you can add or change fields as you go. This property is interesting for integrating different data sources into a single database, or to quickly adapt your data to your user’s needs.
- Read / write performance - Read and write operations in a NoSQL database are fast because there are no data checks. You can process all data operations in parallel without limit, which greatly increases your program speed.
- Scalability - NoSQL databases can distribute data across many servers, making it trivial to scale up or down depending on your needs. It also removes the security risk associated with a single point of failure: from the user's point of view, your database is always on.
- Less features - The structural simplicity of NoSQL databases ends up limiting their features. For example, there are no JOIN mechanisms to query the bare minimum you need from two different collections: you’ll need to query two sub-collections and sprinkle some custom data processing on top.
- Costs - because NoSQL requires more storage to be used efficiently, it is more expensive to run. If a piece of data changes in a NoSQL database, you need to update it everywhere, so it also makes maintaining long-lived data not as efficient. This is why a normal practice with NoSQL is to simply never update data but insert a new row with an updated value entirely, which doesn't help with storage costs.
When To Pick RDBMS Vs NoSQL
Now that you understand the differences between the two, here are six questions to ask yourself to take a pragmatic decision:
1) What’s my company situation?
- If you are at the beginning of a project and remain unsure of what your data needs to look like, use NoSQL. The flexibility will allow you to quickly adapt to any situation without the additional stress of migrating data in a production environment.
- If you are a small or medium company with known data requirements, opt for stability with a relational database.
2) What’s the nature of the data do I need to handle?
- If you provide big data services requiring to extract data from distributed sources, pick NoSQL. Example: an analytics product.
- If you need to implement real-time collaboration features like Google Doc’s, use NoSQL.
- If your data is highly related and not too diverse (e.g less than 50 tables), go for a RDBMS. Example: a website content management system.
- For anything else, either will do fine.
3) What is my budget like?
- To minimize operating costs, you’ll in general be better of with relational databases. The only exception is at scale when upgrading costs won’t probably make your savings worth it.
- If you want to minimize hiring costs, NoSQL is super easy to pick on for any developer. And you can fix design mistakes at a later stage when you can afford database experts.
4) Do I need to emphasize the quality and security of my data?
- RDBMS have built-in data security mechanisms that add an extra security layer to your application. The data will stay consistent.
- You still need to implement data validation in the application layer in both cases.
5) How do I interact with the data 80% of the time?
- If you need to frequently update records, use SQL. Example: a blog post content management system.
- To maximize read / create performance, go for NoSQL. Use case: a notification system.
- Sometimes, the tools you use require a specific technology. Example: you need a SQL database to work in the Wordpress ecosystem.
- Note that it’s easier to start with NoSQL and move to a relational model later, but it’s also easy to move from SQL to NoSQL once you learn it.
Get The Best Of Both Worlds With Rowy
Rowy is a database CMS that brings the best of relational databases to Firestore NoSQL databases in a spreadsheet-like user interface:
Rowy is low-code, built for flexibility. It’s based on NoSQL, but it also offers relational features to increase data consistency like default values, required fields, or views. It’s also free, so don’t hesitate to try it out to get the best of both worlds.