Every business sits on a mountain of data. When that data is properly organized and maintained, it can tell you where you can cut costs, how you can improve your product or service, what your customers like or don't like, how you can increase employee retention rate, and much more. But you need the right database management system to find these gems in your mountain of data. That means you need to understand the difference between SQL and NoSQL.
SQL has long been the world's most popular way of organizing and accessing data. It is the default database management system for most projects. But that doesn't mean you should neglect NoSQL, which is a much better option than SQL for some use cases. In this article, we will explain what SQL and NoSQL are and which system you should use in what scenario.
SQL stands for Structured Query Language. It has been around since the 1960s and is built around a relational data model, which means that your data needs to fit in tables with columns and rows. These tables will typically have constraints that enforce consistency across your data. For example, you can program a particular column to accept only an integer in its rows.
But SQL isn't just a way to organize your data. It's also the name for the language you use to query that data. SQL is standardized, easy to learn, and powerful. Different relational database management systems (RBDMS) like MySQL and PostgreSQL use variations of SQL to query data in different ways, but they're easy to learn once you know SQL.
All SQL databases are ACID-compliant to make sure data is properly recorded and valid even in the face of power failures, errors, and other problems. ACID stands for:
- Atomicity: Every entry into a database is treated as a single unit that either succeeds or fails completely. When it fails, the database remains unchanged. When it succeeds, the database is updated.
- Consistency: All new data must follow the constraints, cascades, and triggers of the database. No new entry can corrupt or challenge the integrity of the database.
- Isolation: Data that is entered concurrently will see the database in the same state as if the data had been entered sequentially. The level of isolation determines the visibility of this process to the database's users.
- Durability: Once a transaction succeeds, it stays in the database no matter what. Power failures, crashes, or anything of the like will not revert the state of the database. This means that completed transactions are held in non-volatile memory.
The final point is about scaling. SQL is most easily scaled vertically, i.e. by upgrading the machine that hosts the data. Chuck in more RAM, storage, and CPUs to improve the performance of an SQL database. While it is possible to scale an SQL database horizontally, by sharding or partitioning the database, it's not nearly as easy as scaling things up vertically.
NoSQL is actually an umbrella term that covers many different ways of organizing data, including organizing it in a table structure like you would a regular SQL database. That's why NoSQL is sometimes referred to as Not Only SQL, because it can support SQL-like structures and queries in some scenarios.
But the underlying thread that connects all NoSQL implementations is non-relational data, or data that isn't particularly structured. A NoSQL database is most commonly a document that you can easily add new data to without having to worry about structure or constraints. Two NoSQL database services that use this document structure are MongoDB and DynamoDB.
Another way to organize data in a NoSQL database is with a graph, where all data points are intricately connected. There's a reason these types of NoSQL databases are popular with social media companies.
Different from an SQL database, NoSQL databases can easily scale horizontally. You just add more partitions. Sometimes, you don't even have to do anything; The vendor will do it for you. That's the main benefit of a NoSQL database: it's extremely performant and highly scalable in comparison with an SQL database.
Your default choice should be SQL. Its flexible query language makes it easy to find information, its constraints keep your data clean, and its relational data model allows you to connect separate SQL databases. It is a well-documented database system with plenty of support to be found online.
However, NoSQL absolutely has its use cases. Here's when you should consider it:
- When you have large amounts of unstructured data. NoSQL can handle a wide variety of data types, from documents to graphs to key-value pairs. Its ability to scale horizontally means that your database stays performant regardless of the unstructured nature of your data.
- When you need flexibility. NoSQL databases don't have a rigid schema you need to follow. This means you don't have to spend too much time setting it up. It also means you can introduce a new data field without having to modify an existing database schema. Of course, the cost of this is often inconsistent data.
- When scalability and availability are of the utmost importance. NoSQL databases are extremely fast, which makes them ideal for social networks and real-time applications like online gaming or instant messaging.