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Should You Use NoSQL Or SQL DB Or Both?

Amit Ashwini
November 9, 2017

The argument around SQL vs. NoSQL is nothing but the comparison of relational vs. non-relational databases. The difference lies in how they’re built, the kind of information they store, and how they store it. Relational databases are structured, while non-relational databases are document-oriented and distributed. For more than four decades now, the Structured Query Language (SQL) databases have been a primary data storage mechanism. With the growing popularity of web applications and open-source options like PostgreSQL, MySQL, and SQLite, usage exploded in the late 1990s. NoSQL databases have been recently gaining traction with popular options such as MongoDB, CouchDB, Redis and Apache Cassandra, even though they’ve existed since the 1960s. End of the day, both SQL and NoSQL does the same thing, store data. Except, their approaches differ. Despite its rising popularity, NoSQL is not a replacement for SQL. It’s an alternative. Some projects are better suited to using an SQL database, while others work well with NoSQL. Some could use both interchangeably.


Structured Query Language (SQL) happens to be the more structured, rigid way of storing data, like a phone book. For a relational database to be effective, you’ll have to store your data in a very organized fashion. SQL databases remain popular because they fit naturally into many venerable software stacks, including LAMP and Ruby-based stacks. These databases are widely supported and well understood, which could be a major plus point if you run into problems.

There’s no one-size-fits-all solution when it comes to database technology. That’s why most businesses rely on both nonrelational and relational databases for different tasks. There are still many situations where a highly structured SQL database is preferable even as NoSQL databases are gaining popularity for their speed and scalability.


  • ACID (Atomicity, Consistency, Isolation, Durability) compliance reduces anomalies and protects the integrity of your database by suggesting precisely how transactions interact with the database. Often NoSQL databases sacrifice ACID compliance with processing speed and flexibility.
  • Your data remains unchanging and structured. If your company isn’t seeing a massive growth, which would require more servers and you’re only working with consistent data, then there’s probably no reason to use a system designed to support high traffic volume and a huge number of data types.
  • These tools come with better support, product suites and add-ons to manage these databases, due to the amount of time they’ve been on the market.


  • The main problem with SQL is scaling it as your database grows. You see, even though scalability is usually tested in production environments, it’s often lower than NoSQL databases. Sharding is quite problematic as well.


If your organization is dealing with massive amounts of unstructured data and your data requirements aren’t clear at the outset, you probably don’t have the luxury of developing a relational database with a clearly defined schema. You get much more flexibility than its traditional counterparts, with non-relational databases. Picture non-relational databases as file folders, assembling related information of all types.


  • The real NoSQL motivator here is Big data, driving the popularity of NoSQL databases like CouchDB, MongoDB, Cassandra, and HBase. NoSQL databases ensure data doesn't become the bottleneck when all of the other components of your server-side application are designed to be seamless and fast.
  • You can store huge amounts of data with little to no structure. Also, NoSQL database sets no limits on the types of data you can store together, allowing you to add more new types as your needs change. And you can store data in one place without defining the type of data in advance with document-based databases.
  • Cloud-based storage is a good cost-saving solution, although you’ll have to spread the data across multiple servers to scale up. NoSQL databases are designed to be scaled across multiple data centers out of the box without much headache.
  • You don't have to prep NoSQL data ahead of time. The non-relational nature of a NoSQL database lets you quickly create a database without having to develop a detailed database model, saving you a lot of development time.


  • The NoSQL community lacks the maturity of the MySQL user base since it is relatively new. While the community is rapidly growing, for now, SQL database management systems like MySQL is hard to compete with for its vast network of highly experienced end users.
  • A major issue with NoSQL databases is the lack of reporting tools for performance testing and analysis. With SQL, on the other hand, you can find a wide range of reporting tools to help you prove your application’s validity.
  • You’ll face compatibility issues with SQL instructions. In the query language, new databases use their own characteristics, and they’re not yet 100% compatible with the SQL used in relational databases.
  • There’s a lack of standardization. There are a lot of NoSQL databases and still no standard, like the ones that exist in relational databases. This lack of standardization of NoSQL can cause a problem during migration.


NoSQL databases are becoming a major part of the database landscape today. With their advantages, they can be a real game-changer in the enterprise arena. For companies looking to integrate Big Data, the lower cost, easier scalability, and open source features of NoSQL make for an appealing option. Even so, NoSQL happens to be a relatively young technology without the set of standards SQL databases like MySQL offer.  Some believe NoSQL is the way of the future, whereas others are concerned by its lack of ACID compliance and standardization. Eventually, your complex business needs and the volume and variety of data it consumes will dictate the choice between SQL and NoSQL.

 For what its worth, for most projects you can afford to have a non distributed, scalable relational database as the single point of truth in your system. It's an easy way to keep data coherent and keep complex queries possible. I hope this helps, but keep in mind, every project is different, and ultimately, you need to see what fits your needs best. Although, whatever choice we got with, we developers are quite adept at justifying our technological choices, regardless of how good they are. But I’d suggest exposing yourself to new technology after weighing its risks and advantages.
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