A common skill listed out in job requirements for data science professionals in ‘Advanced SQL’. However, is it still a relevant skill set in the arena?

These days there is no dearth of advanced tools that can be used to analyze and manipulate the data as necessary that the relevance of SQL skills for data science professionals becomes an important question. SQL is super useful not only for data management, but can also work wonders for data analysis. But the new tools on the block, such as, Python, PowerBI, Tableau, etc. are becoming increasingly popular among users, threatening the relevance of SQL in data science.

Now, we have amazing programming languages, such as, R and Python, which can help with data management and putting forth the initial query to collect data, as well as carrying out other processes such as joining, aggregating and data cleaning in an effective manner. These are basic SQL activities, which can now be performed and implemented a lot more smoothly with much fewer lines of code, thanks to these programming languages. In contrast, advanced SQL performed with windows functions and sophisticated joins can be carried out without any hassles with the general purpose programming languages that have been used traditionally.

SQL is indeed much faster to use, owing to the fact that it is directly operated on the source, without making a copy of the information. But, the interpretation of the code in SQL can also be difficult if the query involved has too many sub-queries. These sub-queries are essential to be able to carry out sophisticated tasks, but it also makes the process of writing the requisite code a really strenuous job.

How are Python, R and Dashboards better than SQL?

When it comes to in-depth analysis, SQL might be a little out of its depth, though it still finds widespread application for carrying out functions like filtering data to extract information from data silos. Once this is done, the data is read into data frames in order to process the data further. Nowadays, a data science professional runs far more complex queries for gathering the desired information directly, instead of requesting the required data with simple SQL queries, and then going on to filter it with the panda’s data frames. The complexity of these queries and sub-queries being run by the data science professionals can be reduced considerably if they write simple commands, then extract the data, and then pre-processing it with Python or R. The only drawback here would be that in this case, data science professionals would have to handle really large volumes of data.

In essence, basic SQL does not have any replacement in the process, though advanced SQL applications do find alternatives in Python and R. Until recently, SQL was very commonly used to generate reports, making it essential for data science professionals to be well-versed in SQL for accomplishing their tasks. However, reliable dashboards such as PowerBI and Tableau are commonly used across organizations, which perform the same functions that SQL earlier used to, without any lengthy codes or complex queries and sub-queries.

Despite this, organizations continue to seek data science professionals who have the requisite SQL skills. The biggest reason behind this is the speed offered by SQL. In any organization, time is of essence, and if the company is engaged in developing software for which time-savings are a key element, then the shortest of delays can be a big deal-breaker for customers.   SQL then plays an integral role in improving the customer experience by reducing the times involved, while also fine-tuning the queries which would help put a rush on the process of requesting the data. So, Python, R, Dashboards, etc. may have an edge over SQL in terms of ease-of-use and lesser complexity, but SQL more than makes up for it with its performance. When speed becomes a paramount importance, SQL is always favored against the multitude of programming languages and dashboards, if the focus is on speed. However, if the focus for the organization is on decision-making, then the different new programming languages and dashboards will be preferred over SQL.

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