TMCnet Feature
March 30, 2020

The Importance of SQL in Data Science

Data is ballooning on a global scale, with hundreds of zettabytes of growth anticipated over the next half-decade. This means that the role of data science in wrangling the almost unending torrent of information will become increasingly important.

Many veterans of this discipline would argue that SQL is essential to the perpetuation of data as a commodity in the digital age. But what factors make it so important today?

The basics

While it may be over four decades old, SQL (structured query language) remains relevant in the 21st century thanks to a number of key advantages that it offers over the alternatives.

First and foremost it is accessible to almost anyone, since it relies not on some obtuse skillset or arcane coding strings but instead harnesses declarative statements to create queries. As a result, anyone who wants to begin a career in data science should be able to learn about the ins and outs of SQL fairly quickly, which is good news because of its cornerstone status in this profession.


At the simplest level it is possible to catalogue and analyse data using spreadsheet software, although this is only really relevant in cases where the volumes of information being managed are minimal. In the context of the rapid rise of big data, it makes far more sense to let SQL bear the burden, since it is far better equipped to handle very large datasets without crumbling under the pressure.

Of course monitoring SQL servers is still essential to looking out for inefficiencies and optimising performance, no matter the scope of the particular system in question; it is simply the case that SQL is naturally a better fit for mammoth data-crunching duties.

Transferable skills

From a career perspective, any data scientist who learns to bend SQL to their will can reliably expect to find employment across a broad selection of industries, since this language is used to orchestrate databases in everything from healthcare to finance and beyond.

It is worth noting that there are actually a variety of different SQL iterations to consider in this context as well, with distinctions between them meaning that there may be a learning curve to overcome when changing jobs. Even so, the fact that the underpinnings of platforms like Microsoft (News - Alert) SQL Server and Oracle Database are so similar is a boon.


The aforementioned accessibility and scalability of SQL make it even more of an asset to data science at a time when there are so many potential operating ecosystems that it might be expected to occupy.

While in the past it could reliably be expected that databases would be running on local hardware, the age of cloud computing, remote data centres, hybrid setups and superfast connectivity has changed the game significantly.

Thankfully SQL has proven more than capable of keeping up with this pace of change and it is possible to deploy it for the purposes of data science in almost any configuration, whether a database is housed on a single dedicated server or exists on a VM in some far-off facility.


When we covered the simplicity of SQL earlier, it might have been possible to mistake this for a limiting factor rather than one of its enduring strengths. As well as making it less taxing for beginners, the ability to master SQL without tearing your hair out is a real advantage, since it means that the best data scientists can achieve some very impressive things in a short timeframe.

SQL is well equipped to order, retrieve and alter information within a database and the more experience you have, the more you will get out of it.

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