Sorting the SQL vs NoSQL database debate
you Selecting the appropriate database can place developers in a bit of a conundrum, especially if choosing the right datastore can simplify your application. The SQL vs NoSQL database debate is a common one, yet this doesn’t mean it isn’t relevant. Quite the opposite: there is always new information you can add to support developers.
First off, let’s start by understanding how each one works to help you decide what’s best for your application.
SQL stands for “Structured Query Language”, and it’s a language that allows you to write database queries in a specific form. It’s not a general-purpose programming language such as Python. SQL’s sole purpose is to access and manipulate data. This powerful combination of specific features or commands, and parameters that you create is used to handle relational databases.
A relational database works with certain assumptions or in a certain way, and it supports the SQL language. How does it work? Every database works with tables, which act as a “data bin” of sorts. Information within each table is organized using a specific schema, and this schema is defined within fields. Every single data entry occupies a row within this table, and should (ideally) fill each of the existing columns or fields.
In a SQL world, you would work not with one table, but with a series of tables that are related to each other. Hence the name, relational database. Relational databases refer to the relational model of data management created by an IBM researcher in the early 1970s. Since their creation, relational databases (and SQL databases, in particular) have evolved to solve their initial limitations.
How is SQL structured?
As we mentioned in the previous section, all SQL databases require a predefined schema. Since all data must follow a very specific structure, the ability to consider potential modifications is reduced. Also, for an SQL database to perform adequately, developers must design the data models carefully. Careless database designs result in systems that resist evolution and require a significant amount of downtime.
It’s also important to refer to the relational nature of the data. That is, data that is distributed and connected via multiple tables. The SQL language has the ability of querying these relations. How? By using specific commands that retrieve data in a single result set. Of course, the more complex these relations are, the longer it would take to retrieve the data.
One of the most reputable aspects of SQL is its efficiency when querying and manipulating data. SQL is capable of handling highly complex queries and only requires you to use predefined schemas to structure your data. Because of its efficiency and simplicity, it is often a good choice for businesses. People can use it without requiring any technical help.
In terms of scalability, SQL databases can expand vertically but not horizontally. This means, you can increase the load by increasing RAM or CPU, but you cannot add more information to the tables. A database that allows horizontal scalability can add more data by adding more servers to the pool of resources. It is incredibly hard for SQL databases to do the latter.
And now, NoSQL
The word NoSQL, which stands for “Not Only SQL”, was re-introduced in 2009 at an event about new technologies. This spiked an interest in research and development, and by 2011 the NoSQL ecosystem was thriving. Some of the most well-known names nowadays include MongoDB, Cassandra, RavenDB and Couchbase – but there are many, many others.
NoSQL, as opposed to SQL, is mainly recognized for its availability and horizontal scalability. Availability, in this case, refers to the percentage of time a system is operating correctly. Usually, NoSQL technologies more availability than relational systems. There is a trade-off, however. The more availability a technology has, the less consistent it is.
What do we refer to when we talk about consistency? Data consistency is related to transactions, in the sense that affected data can only change in specific ways. In this sense, SQL systems are very consistent, as they use structured schemas that allow very specific data inputs. In NoSQL ecosystems, consistency varies from one system to another. This characteristic can be fine-tuned as systems evolve and mature.
Because NoSQL databases usually need additional data processing and lack a declarative query language, queries are handled by developers. When it comes to queries in a NoSQL world, these depend a lot on the database selected. Some of them use a JSON documents database to request data while others create query functionalities directly into the application layer. Regardless of the solution, NoSQL queries require more in-depth knowledge.
When it comes to scalability, NoSQL databases are capable of adding more nodes instead or upgrading hardware (as opposed to SQL scalability). Such technology can increase the amount of data it can handle, as you can add nodes horizontally. This is why NoSQL databases can handle large volumes of data without affecting performance.
SQL vs NoSQL database: A questionnaire
How do you know when to use a SQL or NoSQL database? Across the Internet, you’ll find multiple solutions to this question. And if we’re honest, solutions to this question vary in specificity and simplicity. But before making any decisions, you should always consider three aspects: the type of data in your database, the amount of data you’re going to handle and how the database is going to be managed.
This is why it’s important to ask yourself:
- What type of data are you going to handle? Is it structured or unstructured?
- Do you need to scale? How much will data grow over a specific period of time?
- How much flexibility do you require?
- Is consistency and stability important for your project?
- How many people are going to handle the project? Does anyone in your team have a background in the language your database is going to use?
Although choosing the right database is not always an easy decision, there are many resources and options out there to help you select the appropriate one. This article is not an exhaustive list. It’s objective is to provide a good overview of what you should consider.
A couple of resources
Would you like to learn more about SQL vs NoSQL databases? Here are a couple of links:
- A 20-minute video tutorial on all you need to know about SQL and NoSQL databases.
- A 2016 academic paper in The Journal of Big Data on characteristics of NoSQL databases.
- 8 techniques for using SQL in data science and analytics
- The top 20 courses you can take on Coursera if you wish to learn more about SQL
- A SQL vs NoSQL database cheatsheet for Azure, AWS and Google Cloud