Ask a developer to explain the difference between SQL and NoSQL databases, and they’ll probably launch into a high-level conversation about the characteristics of relational and non-relational databases. And while that is the most notable difference between the databases, you might still be wondering what exactly that means for you.
At some point in your coding journey, you’ll need to learn to use a database, explains David Patlut, Curriculum Manager at Codecademy. “Anybody who's getting into programming is going to run into using a database, because you have to persist [keep] data on any kind of application that you build,” he says.
Differentiating between the types of databases (namely SQL and NoSQL) is a valuable skill for a couple reasons. If you’re building your own app, you’ll have to choose the right database technology for your budget and needs, explains Ben Stone, Senior Curriculum Developer at Codecademy. And if you’re applying for jobs as a Software Engineer, you might be asked to explain the difference between relational and non-relational databases. “It’s a pretty common interview question, so you need to be able to articulate and compare and contrast them,” Ben says.
3 important differences between SQL vs. NoSQL
For starters, when we talk about “SQL vs. NoSQL,” we’re referring to the way data is managed. SQL (Structured Query Language) is a language that you can use to manipulate and query data in a relational database with predefined categories. (Developers often refer to relational databases as just “SQL,” because you have to use SQL to query them.) NoSQL is short for “not-only SQL,” and it refers to any other database that doesn’t store data in tables or queries data by other means than SQL. Here are the main differences between SQL and NoSQL databases that you should know about.
SQL and NoSQL databases structure and organize data in distinctly different ways.
A SQL, or relational, database enables us to store and access data with clearly defined relations; in other words, each item in a table is connected to the others in some capacity. For example, picture an Excel spreadsheet with people’s names and addresses, or a table of dates and average daily temperatures — in these tables, every field (e.g. temperature) is directly associated with another (e.g. dates). Some examples of relational database management systems (RDBMS) include MySQL, PostgreSQL, and Oracle Database.
With a non-relational database, data isn’t beholden to relationships. “A non-relational database is typically much more flexible than its relational counterpart,” David says. “You can still build relationships, but they’re easier to adjust as the use-case of the database changes.”
Non-relational databases can take a couple different forms, but the most common type is a document-based database, where information is represented in a structure similar to JSON. MongoDB is a popular document database that’s a great choice if you’re starting out, because it’s a general-purpose NoSQL database. "Because MongoDB is considered a general purpose database, it means it can be used for a variety of use cases,” David says. “It can help do most things people want to accomplish with their data.” You can learn MongoDB’s basic functions in our free course Learn MongoDB.
Other types of non-relational database formats you might encounter include graph (like Neo4j), key-value (like Amazon’s DynamoDB), and column-oriented hybrid (like Amazon’s Redshift). MongoDB can actually implement most of these types of non-relational databases.
NoSQL databases tend to be more flexible than SQL ones, because data doesn’t need a predefined schema. “If you're looking to build applications quickly, MongoDB is perfect. You don't need to spend much time defining your structure or relationships, you can just put it in the database and decide later how you’d like to change it,” David explains. NoSQL databases are well-suited for situations when your data is only partially structured or you need to quickly build and scale something.
Interestingly, NoSQL databases came on the scene in the 2000s and they were built to fit the Agile framework for software development. NoSQL databases can easily adapt and evolve with the iterative changes and updates that are inherent to the Agile approach. SQL, on the other hand, has been around since the ‘70s, and has a well-established presence in the developer ecosystem and thorough documentation.
SQL databases are more rigid. With most SQL databases, you’d need to manually update all of the fields to ensure that they have the required schema to make changes, or complete lots of migrations to reformat data, which can be tedious, Ben says.
That said, SQL’s squareness and precision can be a positive thing. All relational databases must follow four specific criteria to ensure their reliability: atomicity, consistency, isolation, and durability (aka “ACID”). Taken together, these properties give you peace of mind that your database is going to stay consistent.
(One cool thing about MongoDB is that it supports ACID transactions, even though it’s technically a NoSQL database. And it has a schema validation feature so you can apply more rigidity to your data structure if you need.)
Companies that deal with big data or developers working on enterprise-level applications need to consider database scalability, David says. If an app becomes hugely popular, how will a company keep up with its database needs?
Both SQL and NoSQL databases can handle lots of data, but scale differently. SQL databases are scaled vertically, which means that as a company’s database grows, you have to invest in more (often expensive) server hardware and processing units to handle the increasing load. As you scale in a SQL environment, handling the myriad of tables for data modeling can get unwieldy.
NoSQL was designed to be scaled horizontally, meaning you can add cheaper commodity servers as needed. For this reason, companies that deal with huge swaths of data (including Amazon, Google, and Netflix) often opt for NoSQL databases. With NoSQL databases, you don’t have to deal with table joins, which can be a huge pain in relational databases.
There’s a time and a place for both SQL and NoSQL databases — one is not inherently better than the other. Understanding the nuances between the databases can help you choose the technology that makes the most sense for your needs.
Ready to start learning databases? Here are the courses and paths to check out:
- Learn MongoDB: This free beginner-friendly course will teach you how to structure a document database using MongoDB. It also dives into more advanced operations like atlas clustering.
- Analyze data with SQL: Start building your own SQL databases and get practice answering technical interview questions.
- Design Databases With PostgreSQL: Relational databases can be elegant and sophisticated, too. In this path, you’ll use PostgreSQL to make fast and sleek relational databases. Back-End Engineer Career Path: Learn all of the major technology that a Back-End Engineer uses, including databases. With this path, you’ll be able to design your own PostgreSQL database and better understand software scalability.