Is SQL Harder Than C++?


SQL is the standardized language used for working with relational database management systems. When working with data, is SQL easier or harder to learn and use than other languages such as C++?

SQL is not harder than C++ to learn or use. If anything, SQL’s declarative nature and semantic syntax make it far easier to learn and employ than C++. However, it is essential to note that one is a query language while the other is a mid-level general programming language.

This article will cover SQL and C++’s fundamental differences, providing context for why SQL is easier than C++. Also, it will explore sample situations where both languages are used in tandem.

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Are SQL and C++ Similar?

SQL and C++ share certain similarities. For example, they both have been around for decades. Also, both languages have been standardized by the International Organization for Standardization (ISO). These attributes make both quite popular.

Are there other comparisons in both languages? Directly comparing both languages would be unfair—the reason for this being the different natures of purpose for both languages.

SQL Overview

SQL is a data-driven language primarily used for querying and manipulating data in relational databases. It can be considered a high-level language in that using SQL is hardware agnostic. In other words, using SQL does not require providing any input related to the hardware that is involved.

With SQL, all you need to concern yourself with is structuring the query or command you wish to execute. Additionally, the syntax used in SQL is semantic. This fact makes learning and using SQL much more intuitive compared to other computing languages.

When using SQL, you are primarily dealing with the database schema. This term refers to how the data is organized and how the database architecture is constructed.

Since SQL is used with relational databases instead of non-relational databases, the schema objects involved are primarily tables and columns. Additionally, views, data types, relationships, primary keys, and stored procedures also fall into this realm.

C++ Overview

Unlike SQL, C++ is a general-purpose programming language. You can use it for system programming as well as embedded programming. You can also use C++ to program resource-constrained applications.

In practical terms, this means that you can use C++ to code for operating systems, software, desktop applications, and servers—including SQL servers.

Although C++ has some characteristics of a high-level language, it is commonly referred to as a medium-level language. While you can use it for programming in a hardware-agnostic fashion, C++ retains a significant number of low-level language attributes. C++ can be seen as bridging the area between the low-level realm of a machine-level language and a high-level Language.

When the first stable version of C++ was released in 1985, it was initially meant to be an extension of the C language. However, since then, the number of programming paradigms it covers has expanded to include functional, generic, object-oriented, modular, and procedural programming. Compared to SQL, C++ is a multi-domain programming language, not a domain-specific language.

The expansive use-set of C++ and its inclusion of high-level language features means that code written in C++ needs to be compiled to produce executable programs and applications.

Are SQL and C++ Comparisons Fair?

Once you understand the differences between SQL and C++, it results in questioning whether it is fair to compare both languages regarding their ease of learnability.

Comparing a single-domain programming language to one that applies to multiple domains, by definition, is not equitable. There is so much more you can accomplish with C++ compared to SQL. Therefore, it is a more complicated language with a steeper learning curve. This fact should not be used as a negative counterpoint when comparing C++ to SQL.

If one compares both languages to domains that both share, then you can construct a fairer comparison. In this case, that would involve comparing them in database design, construction, and manipulation.

Why Is C++ Harder Than SQL?

The main reason that C++ is more challenging than SQL is due to the reasons that are outlined above. C++ is a middle-level, general-purpose programming language, while SQL is a high-level domain-specific language.

Being functional in C++ requires learning and developing a complete skillset, while with SQL, it is possible to learn and apply the language in a modular format. In other words, with SQL, you can learn what you need to use as you go along.

It is possible to become fully functional with SQL across tiered levels. You can master SQL as a beginner, similar to the way that a general data analyst would. You can also master SQL on a more intermediate or advanced level, how a database architect or advanced data science or machine learning professional would.

Mastering SQL at a basic level involves learning its syntax and command structure. Practical knowledge involves formulating simple queries. You can acquire SQL at this level in live or online workshop-style courses. Progressing with SQL can also be conducted at this same modular pace.

C++, on the other hand, requires learning the basics of the language, such as becoming familiar with its syntax, plus becoming familiar with how code is structured with the language. Since the number of identifiers, keywords, trigraphs, and variables for data types is more extensive in C++, this requires a more comprehensive learning approach.

How Do SQL and C++ Work Together?

Taking on C++ as a beginner compared to SQL can seem daunting. When compared strictly along the lines of getting started with a programming language, yes, C++ will be more challenging. 

However, when one looks strictly at the domain of data querying and manipulation, the use-case scenarios between SQL and C++ become more equitable in terms of difficulty. In some cases, C++ may make a process more straightforward compared to tackling the same query with SQL alone.

When dealing with data, SQL will always be the default language of choice if that data resides in a relational database. However, the use of non-relational databases has been increasing in recent years. Also, SQL has an Achilles heel when it comes to complex analytical queries.

In other words, SQL is ideal for querying enormous sets of relational data but does not perform well when the data calculations required are complex. 

It does not mean that SQL cannot handle complex querying and computations. Doing it in an optimized and efficient manner to avoid significant lags in response time and over-saturation of processing resources requires more advanced knowledge of its functions and operators.

Even then, some of the advanced querying and data analysis models of today require custom functions built with external languages, such as C++. In these cases, using C++ to access a relational database is nowhere as complex as mastering the C++ language. The majority of proprietary and open-source database vendors have API libraries that make this connection very straightforward.

In this way, you can use SQL queries to garner the data needed for analysis but conduct complex calculations using the application you built and compiled with C++. The result is an optimized workflow that maximizes the use of the inherent efficiencies provided by both SQL and C++.

Integrating a C++ application for computational analysis of data in conjunction with SQL is relatively simple from an end-user perspective. Many data professionals use C++ in this manner without necessarily having to deal with the challenging aspects of learning C++ to develop those applications.

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Conclusion

As can be deduced from the information presented here, SQL, in a general way, is far easier to learn than C++. However, a lot of this has to do with the different nature and scope of both languages. There is more that you can do with C++ than querying and modifying data. As such, the learning curve is going to be steeper.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. I interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. High level programming languages — Chemeketa CS160 reader. (n.d.). Chemeketa CS. https://computerscience.chemeketa.edu/cs160Reader/ProgrammingLanguages/HighLevelLanguages.html
  2. Database schema. (2003, October 21). Wikipedia, the free encyclopedia. Retrieved February 19, 2021, from https://en.wikipedia.org/wiki/Database_schema
  3. Data-driven programming. (n.d.). https://homepage.cs.uri.edu/~thenry/resources/unix_art/ch09s01.html
  4. David-Engel. (n.d.). Connection libraries for Microsoft SQL database – SQL server. Developer tools, technical documentation and coding examples | Microsoft Docs. https://docs.microsoft.com/en-us/sql/connect/sql-connection-libraries?view=sql-server-ver15
  5. General purpose programming language – Javatpoint. (n.d.). www.javatpoint.com. https://www.javatpoint.com/general-purpose-programming-language
  6. High-level languages – Types of programming language – AQA – GCSE computer science revision – AQA – BBC bitesize. (n.d.). BBC Bitesize. https://www.bbc.co.uk/bitesize/guides/z4cck2p/revision/1
  7. History of C++. (n.d.). cplusplus.com – The C++ Resources Network. https://www.cplusplus.com/info/history/
  8. International Organization for Standardization. (n.d.). ISO. https://www.iso.org/home.html
  9. The top data science skills. (2020, March 5). Maryville Online. https://online.maryville.edu/blog/data-science-skills/
  10. What is medium-level language (MLL)? – Definition from Techopedia. (2012, July 5). Techopedia.com. https://www.techopedia.com/definition/20789/medium-level-language-mll

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Daisy

Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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