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Are you planning on taking the leap from SQL to NoSQL and wondering whether it’s the right choice? If so, you’re not alone.
Many businesses are considering this move, as NoSQL databases offer several advantages over SQL databases. However, there are also some disadvantages to consider. So, how do you decide whether SQL vs NoSQL databases is the right move for your business?
The difference between SQL and NoSQL databases comes down to comparing relational vs. non-relational databases. Deciding when to use NoSQL vs SQL depends on the kind of information you’re storing and the best way to store it. Both types store data differently.
Asking NoSQL or SQL is akin to asking the hotly debated internet discussion, “Is it Yanny or Laurel?” People want to give definitive answers, but the real answer is that it depends on what you’re building, constraints dictated by who you’re building for and the end state you’re trying to achieve.
Although NoSQL is trending and the adoption rate is rising, it’s not a replacement for SQL. This is an alternative option. Sometimes it’s about choosing one over the other, but many development teams opt to use both.
In this article we will:
Kim Kardashian infamously tried to break the internet, but NoSQL saved us. With the rise of social media, e-commerce, web searches, and the explosion of data, SQL was struggling to manage all requests, transactions, and activities occurring online.
When it comes to NoSQL vs SQL, NoSQL is a better option. It’s designed to manage various types of traffic and data. Yes, SQL came first. It’s used to communicate with relational databases to store data in a highly organized and rigid manner.
NoSQL is derived from “Not Only SQL” which makes it easier to store all different types of data together. It’s used for its flexibility, speed, and scalability in managing large volumes of data.
Today there are many options such as MongoDB, Cassandra, Redis, Couchbase, DynamoDB, and CosmosDB, which are gaining popularity, growing user communities, and quickly adding more and more features.
There are also four types of NoSQL databases: key-value, document, wide-column, and graph stores.
The key is a unique identifier for the data, and the value is the data itself. Key-value stores are simple, efficient, and well suited for storing large amounts of data that does not need to be structured in a particular way.
Document stores are a type of NoSQL database that store data as documents, similar to JSON documents, and each document can contain multiple fields of data, making them flexible and easy to use for storing semi-structured data.
Wide-column stores are a type of NoSQL database that store data in columns, where each column can contain a different type of data, such as text, numbers, or dates, and they are effective for storing large amounts of data that is not frequently updated.
Graph databases store data as graphs, consisting of nodes representing entities and edges representing relationships between entities, making them relevant for storing data that has complex relationships.
One of the main issues with SQL is its ease of scalability. SQL was mostly designed to scale up. What we mean by “scaling up” or “scaling vertically” is adding extra hardware, RAM, processing power, etc. in order to increase capacity. With SQL, we’re limited because we’ll inevitably max out on capacity and scaling up is expensive. Scaling out with SQL is possible but requires extensive effort (partitioning, sharding, clustering, etc.) and cost.
The key difference when comparing NoSQL vs SQL scalability is that NoSQL engines are designed to scale out and leverage cloud computing. When scaling out or horizontally, resources are added to a single node (computer or server). We can have one database working on multiple nodes. Scaling out (or back in) means that the nodes can be easily added and removed. This makes NoSQL a perfect match for the cloud.
Because it can be scaled out, the scalability benefits of the cloud are maximized. For example, you can run SQL on Azure, but you’ll be limited in your ability to scale.
The SQL vs NoSQL system design is an essential consideration for any application that needs to store and manage large amounts of data. By understanding the different scalability characteristics of SQL vs NoSQL databases, you can choose the right database for your application’s needs.
The ability to store large amounts of data flexibly makes NoSQL faster to develop. You can:
Again, this doesn’t have to mean that SQL is slow. If your data is highly structured and you anticipate minimal changes, then there’s probably no reason to use NoSQL. Because SQL is mature and supported by a strong technical community, your engineers won’t run into problems they can’t solve.
With NoSQL, you’re more likely to run into difficult problems without documented solutions, which can lead to delays. NoSQL databases also lack standardization.
For example, both MongoDB and Cassandra DB are good NoSQL databases for engineers new to NoSQL. However, if an engineer first learns MongoDB, they may still struggle with Cassandra DB because NoSQL lacks standards.
SQL is known for its robust features and tools. NoSQL frameworks provide tools to monitor, backup, and maintain NoSQL databases. To date, SQL is still stronger. However, as NoSQL matures, an increasing number of features become available. Here are some examples:
With SQL you can build one script that retrieves and presents your data. NoSQL doesn’t support relations between data types. Running queries in NoSQL are doable, but much slower.
SQL databases are a better fit for heavy-duty or complex transactions because they’re more stable and ensure data integrity.
Define exactly how transactions interact with a database.
If you’re not working with a large volume of data or many data types, NoSQL would be overkill.
It’s difficult to predict how the application will grow over time.
NoSQL databases offer more flexibility in terms of data modeling, allowing changes to be made without the need for extensive code alterations.
The data modeling process is iterative and adaptive. Changing the structure or schema will not impact development cycles or create any downtime for the application.
This is related to the above-mentioned SQL requirements for ACID compliance. For example, with social media platforms, it isn’t important if everyone sees your new post at the exact same time, which means that data consistency is not a priority.
NoSQL makes it easy to store all different types of data together and without having to invest time into defining what type of data you’re storing in advance.
As discussed above, NoSQL provides much greater flexibility and the ability to control costs as your data needs change.
To answer NoSQL vs SQL, we must start by understanding the domain.
What’s the end state that you’re trying to achieve? NoSQL vs SQL in 2023 is often not about one or the other, but about when and where to use each within the same application and ecosystem.
We recently designed an application in which NoSQL made the most sense for many different reasons. However, the application required further investigation.
Instead of overanalyzing the differences between SQL and NoSQL, we decided to use both NoSQL for the web and desktop versions of the application and SQL for the reports. We plan to store the data in NoSQL databases then transfer only the data we need for the reports to an SQL database.
If you’re interested, we explain how we proactively designed a ready-to-scale architecture without bloating the application in another blog.
In this article, we aim to address the questions that revolve around SQL vs NoSQL databases. We then go deeper into more areas such as which type of NoSQL database to choose, which NoSQL framework is the best and why, how and when to port existing SQL to NoSQL, how to conduct a cost comparison, how to support developers, and how to avoid common pitfalls.
NoSQL is evolving and it’s a bit of the “wild west” out there, with options changing quickly. If you’re uncertain when to use NoSQL vs SQL or which way to go or are facing a tough problem, contact us for a consultation.
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