This is confirmed by 70% of the surveyed individuals on the 2016 O’Reilly Data Science Salary Survey, who replied SQL to be highly relevant in their professional context. It even stands out above the R (57%) and Python (54%) programming languages.1 More recently, in 2021, 42.7% of 32,000 jobs listed in Indeed with ‘data’ in the title mention SQL as one of the key skills required.
SQL is still a must-have skill when you’re working towards getting a job in the IT industry. However, why is it so frequently used? And why is it still relevant even though it has been around for such a long time?
Here are some important reasons: first, it would be that companies mostly store data in Relational Database Management Systems (RDBMS) or Relational Data Stream Management Systems (RDSMS) and you need SQL to access the data. SQL allows you to interact with most databases in the industry.
Also, a remarkable point to consider, you need to keep in mind that there are different RDBMS and RDSMS vendors in the industry and each one has SQL implementations that differ from the standards on some of their features and thus are incompatible with each other. For this reason, knowing the standard SQL is a requirement for you to find your way around in the IT industry.
Additionally, SQL has been embraced by newer and robust technologies, such as Hive, a SQL-like query language interface to query and manage large datasets, or Spark SQL, which can be used to execute SQL queries. The SQL used on these newer technologies differs from the standard that you might have learned, but the learning curve will be considerably easier as the same principles still apply.
In short, this is why you should learn SQL:
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It is fairly easy to learn and there are a ton of interactive tutorials online. e.g to name a few:
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It is a smart investment of your time as it follows the “learn once, use anywhere” principle.
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It is an excellent addition to programming languages. In some scenarios, it is more efficient to write a query than to access it through an API or custom code.
Sources:
1 https://www.oreilly.com/radar/2016-data-science-salary-survey-results/