Is Groovy a Viable Language for Data Science Applications? 5 Pros and Cons
Choosing the right programming language can make a remarkable difference in data science applications. While the industry standards are Python and R, some data scientists have branched off to use others they prefer. One such possible alternative is the Groovy programming language.
Apache Groovy is an object-oriented, dynamic syntax for the Java platform. Though it was initially released in 2003, it’s seen several improvements over the years and has become increasingly popular. Of course, popularity alone doesn’t necessarily mean it’s viable for data science.
Here’s a closer look at the pros and cons of using Groovy programming for data science applications.
Pros of Using Groovy Programming for Data Science
Overall, Groovy has several advantages that can make it a helpful data science language. These three are some of the most notable.
1. Flexibility
Perhaps the most significant benefit of Groovy for data science is its flexibility. The language is interoperable with other Java libraries, so data scientists can easily integrate it into their other Java-based applications. It also supports optional typing, which can help streamline some data science operations.
Compared to other Java languages, Groovy offers a wide range of options like null coalescing and string interpolation. While not all data scientists will use these functions, having the option makes Groovy more applicable across a broader range of scenarios. The language’s flexible syntax also makes it ideal for creating domain-specific languages (DSLs), helping customize its applications.
2. Quick Learning Curve
Another benefit of Groovy for data science is its relatively easy learning curve. Groovy originally began as a way to simplify some aspects of Java, and it still adheres to that goal. The language is concise, readable, and interoperable, making it easy for data scientists familiar with Java to pick it up.
That quick learning curve can be crucial in a field as fast-growing as data scientists. New programmers or analysts can get accustomed to using it without extensive training, helping them deliver value in a shorter time. That also makes it easy to switch over to Groovy from another, more popular language like Python.
3. Security
When 47% of data breaches cost businesses $500,000 to remedy, security is a leading concern for data science. As an open-source language, Groovy has the advantage of security recommendations and fixes from the community. Over time, these community-generated security improvements have made the language a comparatively secure option.
Groovy has a dedicated cybersecurity team and a bug tracker to help users report any potential issues. These features help it resolve any security problems quickly, keeping applications that rely on Groovy safe. Like with any language or program, though, users must remember to keep it up-to-date at all times.
Cons of Using Groovy for Data Science
While Groovy has some significant advantages, it does come with some caveats as well. Here are two of the most important to note.
1. Prevalence of Python and R
Groovy’s biggest weakness is simply the prominence of other programming languages. While it’s compatible with other Java libraries, users must port any applications that use non-Java languages like Python or R. That’s a relatively straightforward fix, but the sheer popularity of these other languages is hard to ignore.
According to a recent survey, 88% of data science students said their educators taught them to use Python. Similarly, 63% of data scientists say they use Python frequently. Considering how common Python-based applications are, having to port them every time to use Groovy can be a considerable time waster.
2. Easy to Overcomplicate
Another disadvantage of Groovy in data science stems from one of its strongest advantages: its flexibility. While Groovy’s vast range of tools and features make it applicable across many applications, this can be an issue for new data scientists. Too much freedom can lead to users quickly overcomplicating their scripts.
After over-deploying these tools, users could have a hard time finding the source of any issues that arise. Considering how crucial it is to re-check code in data science, this could cause significant disruption. Users must know how to use all of these tools properly to avoid these scenarios.
Groovy Is a Viable Option for Java-Based Data Science
Overall, Groovy is sufficient for Java data science applications if users understand how to employ its features properly. If a team deals mostly with clients or other applications using Python or R, it may not be the most convenient option. Similarly, it may not be ideal for new, less experienced users, despite its fast learning curve.
If teams use many Java-based applications and are relatively experienced, Groovy can be a helpful alternative. While it has some shortcomings, it’s an excellent overall programming language.
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