Is SQL Easier Than Java? It is Not a Simple Answer

Java and SQL are languages that have been around for decades and that are still in high demand. Both being coveted by employers in the job market, it is a fair question to wonder: which one is easier to learn?

SQL can be construed as easier than Java. SQL is a domain-specific language for managing data in relational databases, while Java is a general programming language. Furthermore, SQL is a declarative language with its syntax semantic in nature, adding to its comparative simplicity.

This article will cover the underlying reasons why SQL can be a more straightforward language to learn than Java. It will also point out certain caveats regarding how the definition of ease-of-use and ease-of-learning can vary for different use case scenarios.

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The Basics About SQL and Java

At first glance, the question of which language, Java vs. SQL, is easier to learn might seem like an apple to oranges comparison.

After all, SQL is primarily a data query language, while Java is intended for general development. It would help if you found some common ground for the comparison to have a validity of purpose. Understanding some basic information about each language is necessary for this.

What Is SQL?

SQL is a query language that has become the default standard for managing, querying, and manipulating relational databases. It has been in existence for over four decades. It has become the de facto language for any development that involves data manipulation.

While you can use SQL to construct complex queries for various analytical purposes, its general command structure and syntax are semantic. Being a declarative language, this shouldn’t be a surprise. The simplicity of structuring queries in such a way has contributed to the popularity of SQL.

What Is Java?

Java, on the other hand, is a general programming language. Its primary use among developers is for building enterprise-level web applications.

Java is considered to be a very stable language with very few implementation dependencies. Being an object-oriented programming language means that applications can contain data within fields and code in the form of procedures.

As can be expected, Java’s robustness means that its command and syntax structure will be more complicated than SQL.

Is It Fair To Compare SQL and Java?

Again, returning to the fairness of the “apples and oranges” comparison between SQL and Java, should they even be compared?

On the surface, it would seem that there is no benefit in making that comparison. However, when you look at both languages on a more granular level, you will find two key areas that merit that comparison. These two areas are the demand for SQL and Java skills in the job market and how both languages intersect with data-related operations.

Demand in the Job Market

If you are pondering what language is easier to learn between SQL and Java, it would be logical to assume that you are interested in learning at least one of those languages. In most cases, this would be associated with the amplification of your skillset to better position yourself professionally in the job market.

When viewed through the prism of marketable skills, both Java and SQL ranked highly in terms of demand. A study conducted by Burning Glass demonstrated that SQL and Java ranked first and second respectively as the most requested tech skills from potential employers during the first quarter of 2020.

This high level of demand does not appear to be an outlier. It seems to be consistent due to the concurrent need for software and app development along with data-intensive operations such as data analysis, predictive analysis, machine learning, etc.

Having a reference point regarding the ease of learning between SQL and Java is fundamental when analyzing where you want to invest your time resources for professional development. Additionally, it can also help you determine when and if learning both languages would benefit you.

If you intend to enter the job market with a focus on data, learning SQL would not only be easier on a direct line comparison with Java, but it would also make marketing your skills for such job positions easier as well. SQL is simply in such high demand for a wide range of positions covering everything from data analysts to data scientists to database developers.

Their Use in Data-Related Operations

The association of SQL with data-related operations should be apparent. After all, its primary purpose is to query relational databases. However, in the current paradigm of machine learning, artificial intelligence modeling, and big data, relying solely on SQL can inhibit data-intensive operations.

Additionally, the trend towards increasing non-relational databases makes other languages’ presence to manipulate data necessary. The two programming languages apart from SQL most associated with data-related operations are Python and R. However, Java also has a significant presence in that capacity.

If your responsibilities only involve conducting simple queries and basic calculations with data residing on tables in a relational database, basic to intermediate knowledge of SQL would, in most cases, be sufficient. Likewise, suppose your role is only to conduct queries that have already been constructed for you for data analysis and reporting. In that case, rudimentary SQL knowledge may be all you need to perform your duties.

Developing a database architecture, constructing and conducting more involved queries, and gathering data from multiple locations will require more advanced SQL knowledge.

The modularity of SQL knowledge is one reason it is identified as being easier than Java. With SQL, it is possible to market your skills and to be highly efficient at your job solely by mastering the level of SQL that you need. It makes SQL very scalable. It allows you to learn what you need quickly to enter the job market or secure a promotion and later advance your SQL knowledge at your own pace.

Using Java to work on data, however, requires an intermediate to advanced knowledge of Java. Considering that the learning curve is steeper for Java, this means that more time is needed from those wanting to develop the skills required for using Java in a data-related role.

Applying Java in a data role is also more tedious compared to the declarative style of SQL. Java is a statically-typed language. When dealing with data, you must declare all values. The scalability of Java, when used in large-scale applications for data science, makes it worthwhile. However, the rigidity and complexity associated with it also mean that it requires more advanced learning to make it a marketable skill in the data-related job market.

Is “Easier” Always Best?

For many professionals, the ease of learning and working with SQL will make it best for them. However, there will be a subset of data professionals who, due to their role or the nature of the underlying platform that they are working on, taking the time to learn Java, albeit a more involved endeavor, would benefit them.

Of course, you should note that in most cases, when using Java to work with data, a level of SQL knowledge would also be required.

Conclusion

The semantic ease, declarative structure, and learning modularity make SQL easier to learn than Java. There is also a broader need and presence for SQL when it comes to working with relational databases.

That said, it is essential to bear in mind that SQL and Java are different, SQL being domain-specific for relational database querying, and Java being for general programming. It is why you should also weigh the ease of learning SQL against potential applications in other development areas.

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