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Discover the differences between SQL and NoSQL. Spoiler alert: it’s not the word “No”. Although it is in a way? Bonus, find out why Directual is using NoSQL and how you can get a headstart on all things databases faster.
Databases come in many flavors, some of them more tangy than others. To help you understand which is better (if such a thing is possible), we’ve prepared this article. You’ll discover the differences between SQL and NoSQL, and how they can help you on your journey to creating something awesome.
Let's start with SQL.
SQL stands for Structured Query Language. It is a language used to communicate with databases, allowing users to retrieve, update, and manage data stored in relational databases.
SQL was first developed in the 1970s by IBM researchers Donald Chamberlin and Raymond Boyce. It was originally called SEQUEL (Structured English Query Language), but was later changed to SQL.
SQL is a standardized language. This means that regardless of the database management system you are using, the SQL syntax and commands remain the same. Basically, you can transfer what you learned from one database system to another without having to learn a new language. Pretty handy.
“I had a problem, so I thought I'd use SQL. Now I have two problems.” - bash.org
Most websites and web applications rely on databases to store and retrieve information. It is an essential tool for web developers and anyone working with data, including no-coders. However, there is another option…
NoSQL is a term used to describe a non-relational database management system.
Unlike SQL, NoSQL databases do not use tables, rows, and columns to store data. Instead, they use a variety of data models such as key-value pairs, document-based, graph-based, or column-oriented.
NoSQL databases were first developed in the late 2000s as an alternative to traditional relational databases.
NoSQL was initially used by companies like Amazon, Google, and Facebook. They needed a database system that could handle massive amounts of data and scale horizontally. NoSQL databases were designed to do just that. They can easily handle large volumes of data and can scale horizontally across multiple servers.
NoSQL is also not a single product, but a family of technologies that share some common traits. For example, NoSQL databases are often designed to be distributed, meaning that they can run across multiple servers, providing high availability and fault tolerance.
Directual utilizes NoSQL for its own databases, and for a good reason: it’s easy to scale them and help you build a project of any size that doesn’t lag, at all.
Plenty to go around.
As we mentioned earlier, SQL databases use a relational data model, which means that data is organized into tables, with each table consisting of rows and columns. This is a very structured approach to data management and requires a clear understanding of the data being stored.
On the other hand, NoSQL databases use a variety of data models such as key-value, document-based, graph-based, and column-oriented. Result - more flexibility in storing data but also requires more consideration required for the data structure and format.
In SQL databases, the schema is predefined, meaning that you need to have a clear understanding of the data being stored and its structure before you can create a table and start storing data.
This approach is known as schema-on-write. NoSQL databases, on the other hand, use a schema-less approach, where the data is stored as it is received. This approach is known as schema-on-read, meaning that the schema is determined at the time of query, rather than at the time of writing data to the database.
SQL databases use the SQL language, which is a standardized language for querying and manipulating data in relational databases. SQL is a very powerful language, with a rich set of commands for performing complex queries and transformations.
NoSQL databases use a variety of query languages, depending on the data model being used. For example, document-based NoSQL databases often use a language called MongoDB Query Language (MQL), while graph-based NoSQL databases may use a language called Cypher.
NoSQL databases can scale horizontally, meaning you can add more nodes to the cluster to increase performance and capacity. SQL databases, alternatively, are often limited to vertical scaling, which means that they can only add more resources to a single node to increase performance.
For one, it’s what the internet was built upon, but!
Unlike many other programming languages, SQL is standardized. This means that the syntax and behavior of SQL is consistent across different database systems.
SQL databases can be scaled up or down to handle different levels of traffic and data volume. This is because SQL supports vertical scaling, which means you can add more resources (such as RAM or CPU) to a single server to improve its performance.
SQL includes many built-in security features that help protect data from unauthorized access and modification. For example, you can use SQL to define user roles and permissions that control who can view, edit, and delete data. You can also encrypt data in transit and at rest to prevent it from being intercepted or stolen.
SQL databases are designed to minimize the risk of data loss or corruption, even in the event of hardware failures or other disasters. Transaction logs and backups to ensure that data can be restored to a consistent state even in the face of unexpected failures.
SQL is good at ensuring data is consistent and accurate. With SQL, you can define rules that enforce the structure of the data in your database. This helps prevent errors and inconsistencies that can arise when multiple people are accessing and updating the same data.
For example, if you have a table of customer information, you could define a constraint that ensures each customer has a unique ID number. This way, if two people try to create a new customer with the same ID, the database will prevent it and maintain the integrity of the data.
SQL allows you to write powerful queries that can join multiple tables, filter data, and perform calculations—you can extract insights and meaning from large datasets. For example, if you have a database of customer purchases, you could use SQL to find out which products are most popular, which customers are the biggest spenders, and how sales have changed over time. With SQL, you have the power to ask and answer complex questions about your data.
Apart from being a wonderful framework supporting Directual, it’s also boasting the following:
You can choose a NoSQL database that is tailored to what you need, such as real-time analytics, time-series data, or graph data. This can help you achieve better performance and scalability for your particular use case.
NoSQL databases support a variety of data models, including key-value, document, column-family, and graph. This allows you to choose the data model that best suits your needs and use case. For example, if you're building a recommendation engine, you might use a graph database to model relationships between users and products.
NoSQL databases are designed to be highly available, which means that they can continue to function even in the event of hardware failures or other disasters. This is because NoSQL databases are often distributed across multiple servers, which allows them to replicate data and ensure that it is always available.
NoSQL databases are often more flexible than SQL databases in terms of data format and structure. This can make it easier to handle data that is constantly changing or evolving. For example, if you're building a mobile app that allows users to create custom forms, you might use a NoSQL database to store the data, since the structure of the form may change frequently.
First up, let's talk about flexibility and schema-less design.
NoSQL databases do not require a fixed schema, which means that you can add new fields or change the structure of the data without having to alter the database schema.
This makes it easier to handle unstructured and semi-structured data, such as text, images, and videos. For example, if you're building a social media platform, you might use a NoSQL database to store user-generated content, which can vary in format and structure.
Another advantage of NoSQL is its high scalability and support for distributed systems. NoSQL databases are designed to scale horizontally, which means that they can be distributed across multiple servers to handle large volumes of data and traffic.
This makes it possible to build highly scalable and performant systems that can handle millions of users and transactions. For example, if you're building an e-commerce platform, you might use a NoSQL database to store product catalogs and user profiles, which can grow rapidly over time.
Now, if you're looking for a database that can handle multi-row transactions like a champ, SQL is your go-to. It's great for structured data and user-oriented applications that require several join operations. And let me tell you, the SQL schema is something special. It'll help you establish ACID properties and ensure data compatibility. Plus, it makes finding the data you need a breeze!
But hold on! NoSQL databases are perfect for handling unstructured data, like documents or JSON. They're also great for dynamic data without join operations. And if you've got missing data sets that won't impact business efficiency, NoSQL is definitely the way to go.
SQL databases include MySQL, Oracle, PostgreSQL, and Microsoft SQL Server. And when it comes to NoSQL, we've got MongoDB, BigTable, Redis, Cassandra, HBase, Neo4j, and CouchDB.
SQL databases are great for structured data with a fixed schema and support for complex queries, while NoSQL databases are better suited for unstructured and semi-structured data with a flexible schema and high scalability.
But that's not the only thing you should consider. Here are a few other factors to keep in mind:
SQL databases typically require more development time and expertise to set up and maintain than NoSQL databases. This can add to the overall cost of your project. On the other hand, NoSQL databases are often easier and faster to set up and maintain, which can save you time and money.
SQL databases are designed to maintain data consistency and integrity through the use of transactions and constraints. This can be important for applications that require strict data integrity, such as financial systems or healthcare applications. NoSQL databases, though, often sacrifice data consistency for high scalability and performance.
SQL databases are great for complex queries and data analysis, thanks to their support for JOINs and other advanced query features. NoSQL databases, on the other hand, are better suited for simple queries and data retrieval.
Now, one thing you might have heard about NoSQL databases is that they don't always support ACID transactions. That's atomicity, consistency, isolation, durability, for those of you keeping score at home. And while it's true that some NoSQL databases only support single-record atomicity, that's just fine for a lot of applications, especially if you've got your schema design in order.
But, and this is a big but, there are still plenty of use cases out there that require ACID across multiple records. That's where MongoDB comes in. They added support for multi-document ACID transactions in their 4.0 release, and then they went ahead and extended that support in 4.2 to span sharded clusters.
Now, it's worth noting that NoSQL databases tend to be optimized for queries rather than reducing data duplication. So, they can end up being larger than SQL databases. But, with storage being as cheap as it is these days, most people don't really consider that to be a major drawback. And some NoSQL databases even offer compression to help reduce the storage footprint.
Finally, fun fact! You can use both at the same time.
Yes, what about it?
When it comes to choosing between SQL and NoSQL databases on a no-code platform like Directual…you don’t have much choice really, as every no-code platform decides for themselves what kind of database is used.
In our case, it’s NoSQL—precisely due to all the pros outlined earlier. In case you’re curious to see what the entire architecture looks like, here we go:
Directual is built with a micro-service architecture in mind, which is why it’s so fantastic at linear scalability. However, there’s more to it:
A visual interface to create data models is easier and more intuitive than writing SQL schema definitions. However, you still need to think carefully about the structure of your data and how it will be used in your application.
Since you don’t need to manage databases as much and write code at all, Directual can handle everything related to your database in a way that will easily scale to any required level. Otherwise, it’s quite a daunting task.
Because when it comes to building databases, they also must interact with something. With Directual, it’s not a problem at all, since you can create APIs just as easily.
In our recent case studies, we’ve explored how Evidpo managed to scale up their operations with Directual—and rose to a full LMS with 47000+ students—which was no fuss at all.
And on top of that, databases can be very easy to build, too. Check out how it’s done with Directual in our 101 crash course.
When it comes to databases, it really comes down to the type of app you're building and the data requirements it entails. Understanding each database's unique features will help you decide which one is best for your project.
Plus, it's important to consider everything when making your decision. Knowing which database fits your needs can improve performance, ensure data integrity, and ultimately help you create a successful app that’ll smash it on the market of your choice!
So, in the end:
SQL databases are great for structured data, where data is consistent and relationships between tables are well-defined. They're particularly well-suited for apps that require complex queries and transaction management.
NoSQL databases are perfect for semi-structured or unstructured data, where the data doesn't conform to a predefined schema and relationships between data elements are not well-defined. They're also often used in apps that require high performance and scalability, such as web apps and mobile apps.
Pick the one you need, and if you’d rather databases build themselves on their own, go with Directual instead.
Well, it really depends on what you're trying to do with your database. SQL databases are great for handling complex queries and transactions, and they're typically very efficient at doing so. However, NoSQL databases can be faster in certain situations, such as when you need to store and retrieve large amounts of unstructured data fast. Directual actively uses NoSQL databases for all platform activities.
While NoSQL databases have certainly gained popularity in recent years, they're not necessarily replacing SQL databases altogether. In fact, many organizations still rely heavily on SQL databases for their mission-critical apps and backend. So, while NoSQL databases may be a great fit for certain use cases, they're not necessarily going to replace SQL anytime soon.
SQL databases are a great choice when you need to ensure data consistency. They are well-suited for complex apps with lots of relationships between different data points. They are often a good choice when you need to support transactions that involve multiple operations, such as transferring funds between bank accounts.
NoSQL databases are often a better choice when you need to store and retrieve large amounts of unstructured or semi-structured data quickly. They're also great for scaling horizontally, meaning you can easily add more servers to your database cluster to accommodate increasing amounts of data. NoSQL databases often offer more flexibility than SQL databases, since they don't require a rigid schema to be defined upfront
Absolutely! In fact, many projects use both SQL and NoSQL databases to handle different types of data. For example, you might use an SQL database to manage your users and their account information, while using a NoSQL database to store large amounts of user-generated content like blog posts or social media updates. By using both types of databases, you can take advantage of their respective strengths and ensure that your data is stored in the most efficient way possible.
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