This is a guest article by Alex Williams from Hosting Data
Otherwise referred to as Non-SQL or Not-Only-SQL, NoSQL is essentially any sort of database that doesn’t use relational database structure. Of course, that doesn’t mean -- despite a common misconception -- that NoSQL can’t handle relational data. In fact, not only can it deal with relational data just fine, but in some cases may even be superior to SQL.
Mostly the reason for this ease of use is because, with NoSQL databases, the data doesn’t need to be split into different tables. With NoSQL a single data structure can have relational data inside it.
While NoSQL has been around since the 70s, it didn’t really take off until the early 2000s, when data storage became incredibly cheap and there was no longer any need to create complex data models to reduce duplication. Funnily enough, NoSQL doesn’t care one iota for duplicated data, and having duplicates is no problem.
AltexSoft previously explained what relational vs non-relational databases are, so here we will go into more detail on one of them.
What is NoSQL?
NoSQL essentially describes a grouping of philosophical database design blueprints that avoid relationship data storage. This means that NoSQL databases tend to be very specialized and built to purpose and therefore do not necessarily function as a replacement of SQL.
So, describing how NoSQL works can’t go beyond saying things like "non-relational" and "doesn’t use a schema." Although even the latter isn’t necessarily true since you could use a schema. It’s just that in NoSQL you don’t have to.
We touched on a couple of the advantages of NoSQL above, but let's take a deeper dive.
NoSQL pros
Performance. NoSQL databases can often outperform SQL databases simply because all the information is contained inside one database. Whereas with SQL you might have to query data across multiple tables, with NoSQL everything is nestled inside of one, so things move fast. Some forms of NoSQL databases can do 10,000 queries a second, which is incredibly impressive, to say the least.
Scalability. Probably the second biggest positive of NoSQL is that it’s horizontally rather than vertically scalable. With SQL, the only way for you to upgrade the hardware is to buy higher-end and more expensive stuff, such as CPUs and RAM. With NoSQL, that’s not an issue because you can just chuck another shard on and you’ve scaled it. This makes the expansion of NoSQL incredibly cheap and easy, compared to SQL.
Flexibility. Due to the non-rigid nature of NoSQL, it's much easier to test ideas and updates. This is essential in modern applications where fields can vary and data structure changes need to be easy and fast.
Data models. Interestingly, NoSQL is more of a philosophy than a specific data model and has several data models beneath it. As such, these data models tend to be incredibly specialized in specific use cases, allowing them to outperform relational databases. For example, hierarchical databases are great for geospatial information used in geotagging apps, with data storage made even easier due to the 1:N relationship (that is, tree or parent-child).
In terms of disadvantages, there aren’t as many, but they may be considerable depending on the project.
NoSQL cons
Not mature. Probably the biggest downside of NoSQL is that it’s not as mature as SQL. Sure, it’s been around since the 70s and started to become popular in the 2000s, but the truth is that SQL has had massive amounts of investment, time, and effort poured into it. As such, SQL is in a much more advanced state compared to NoSQL.
So what does that mean? Well, simply put it just isn’t as easy to find the kind of information and support on NoSQL as you would with SQL. For example, if you’re looking for an expert to consult on a project, it would be much easier to find an SQL expert than a NoSQL expert. This might be great if you’re looking to expand your skills and make more money as a programmer, not as great for projects requiring the experience.
Requires multiple databases. As mentioned above, NoSQL isn’t as much of a hammer as it is a scalpel. That means that it’s made to be very specialized for specific use cases and not necessarily meant as a catch-all. This contrasts with SQL which is a very generalized model and therefore can fit a variety of needs.
As such, if you’re using NoSQL, you’ll probably end up using multiple types of databases and data models to fill all the niches and use cases. You may even still need to use some form of SQL just to help with streamlining the process.
Huge databases. This one isn’t as big a problem as it used to be, but since NoSQL isn’t designed to remove data duplication, database sizes can become truly massive. Again, with how cheap storage is these days it’s not that big an issue, but it’s something to keep in mind. Also, data quality tends to be an issue with NoSQL and its large databases, which should also be something to be aware of.
NoSQL database types
Hierarchical
As mentioned above, hierarchical databases use a tree or parent-child model for data. Data is stored as a record and then cross-referenced with other records. The type of data in any field depends on the field itself.
If you have some experience with SQL, then you know that it's certainly possible to store data in a hierarchical model in an SQL database, but it’s not necessarily convenient to do so. Modeling hierarchical databases in NoSQL is much easier and straightforward.
Key value store
You can thank Amazon’s AWS for key value stores, as it’s borne out of their ‘Dynamo Research Paper and Distributed Hash tables research.
Essentially this type of database is made for high-performance applications and uses unique keys that store a pointer to associated data. Since both keys and values can be anything, this database is incredibly versatile and flexible and therefore perfect for an online retail giant like Amazon.
Document stores
Interestingly enough, while document stores as a data model is popular in its own right, it’s a subset of key value stores, the main difference being how the data is processed.
Unlike SQL where you're using a schema, NoSQL doesn’t, and therefore XML and JSON don’t need to be wired together unnecessarily. There is even an XML Document Store data model that is made specifically for XML.
This generally provides for a richer experience due to the reliance on document structure for metadata extraction and optimization. There are actually lots of reasons to use document stores over key-value stores
Network
These types of databases, also known as graph databases, are well suited for any information that would go on a graph.
Data storage is done through relationships and nodes, with nodes being the entity and the relationships describing how different nodes are linked. The use case here is mostly data that tends to change often. One especially well-known database is FlockDB and is the one mentioned earlier that can handle 10,000 queries per second.
Object-oriented
As the name suggests, this data model is based on storing information as objects, which can really be anything, and more specifically, transparent.
Object-oriented data models are great for complementing or enhancing relational databases and tend to see a lot of use in web-scale and research. It’s probably one of the better database management systems out there.
Column-oriented
Another data model that is what it says on the box, as this type of model stores data as columns rather than rows. Each row contains its own columns and different rows do not require the same number of columns. As such, this offers a vastly superior search & access data aggregation in terms of speed.
This can be a bit difficult to wrap your head around, so Amazon’s AWS has a great guide on what columnar databases are.
Triple Stores
This data model stores information using subject-predicate-object. In essence, the way this works is that a predicate describes the relationship between a subject and an object, while the latter two function as you’d expect for those items.
Interestingly enough, this type of data model's function is very similarly to the network model, although it is targeted more to semantic queries.
How to learn NoSQL
Truthfully, the best way to learn NoSQL is to teach it to yourself through online resources, and, of course, there are a variety of different resources for different databases.
For example, Tutorials Point has a great course on MongoDB. For DynamoDB, GangBoard has a full-fledged course you can take. Neo4j actually has a series of video tutorials on their website. So really, the best way to learn specific NoSQL data models is to search for learning materials on that particular database.
Also, if you prefer a more guided approach to NoSQL as a whole rather than specific Databases, there are some great edX NoSQL courses.
Let’s end this introduction to NoSQL with a quick FAQ section.
NoSQL: Frequently Asked Questions
Is NoSQL a Replacement for SQL?
No, NoSQL Databases are made to enhance, complement, or fill specialized gaps. They aren’t meant to replace SQL altogether. Again, you can choose to use one, the other, or both. It depends on your application and use case.
NoSQL vs. SQL: Which One to Use?
NoSQL is a model that doesn’t use relational databases, whereas SQL does. More importantly, and something that many often forget, NoSQL isn’t necessarily meant to replace SQL so much as it is meant to enhance or complement it. You can absolutely choose to not use SQL, but that’s not a requirement.
Is Learning NoSQL Essential?
You don’t have to, but you absolutely should. Getting into the dark and dirty bits of data and data models is important to becoming a data scientist, and while you can absolutely just deal with the information post-fact, being able to deal with the database itself would be a big boon for yourself and your career.
When to Use NoSQL?
NoSQL is purpose-based for cases with fast-paced development, high performance, and large scalability requirements.
Conclusion
As you can see, NoSQL is incredibly versatile and has almost unlimited use cases, as specialized databases can fill smaller and smaller niches. That being said, NoSQL is more of a design philosophy and isn’t necessarily meant to replace SQL, especially since SQL is so established in the modern world, it would be near impossible to get rid of it.
No, the purpose of NoSQL is to make our lives easier when handling large amounts of data and to offer data models that allow us to be more efficient and have better performance than a traditional SQL database when dealing with massive databases.
Either way, having knowledge on both NoSQL and SQL is important, especially in a world where the requirement for both is still expanding.
Alex Williams graduated in 2012 from the University of London, majoring in Computer Science. Afterward, he started his own developer agency, helping new business owners set up their websites and expand their marketing reach to the digital field. During that time, he also extended his knowledge of relational and non-relational databases and even briefly worked as a NoSQL developer for a couple of years. By 2019, Alex moved away from writing code and now works as a part-time IT consultant. He also runs his own blog, Hosting Data UK, where he writes about topics that interest him, shares his dev experiences, and explores digital marketing strategies.
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