If you’re wondering which programming language to learn for Big Data, then we got you covered. We’ve covered the best language for big data with the best recommendation of online courses to learn. These courses will help you on the path to becoming Big Data professional.
It may seem like many programs exist in the market today that specialize in handling large caches of data, but in reality, there are only a fair few that lead the industry. These unique languages used to communicate in structures and formats are exceptionally complex technologies that took years to invent. However, with the combined efforts of dedicated and talented individuals worldwide, breakthrough applications now exist.
Much like the differentiation between spoken languages between different geographical settings, programming languages are specified by the programmer who creates them. Common ground in the form of coding preference, IT background, and project goals are inspirations for making them. Anyone aspiring to become a data scientist or data engineer should read on and learn which programming language is best for big data.
Which Programming Language to Learn for Big Data
One of the oldest programming languages is Java, and despite its age, most traditional frameworks for big data revolve around Java’s coding capabilities. Apache Hadoop is an excellent example of a system that has tools capitalizing on Java-based scripts. Companies rely on Java-based ecosystems for their stability and decades-long integration into industry standards.
The veteran advantage of Java over other programming languages is that enterprises have widely used it to the point that any errors or mistakes within coding are easily managed. Production is easier with Java as it has an extensive library of tools and libraries for cross-platform operations, monitoring, and easy re-coding.
The best programming language for big data analytics in the past may have been Java for a long time, but in recent years, that hasn’t been entirely the case. However, let’s take a look at the different pros and cons of this veteran application. Coursera offers Introduction to Java course beginners, make sure to check it out.
- Simplicity is key: The straightforward nature of Java makes it fairly manageably to code, compile, debug, and learn daily. The automated allocation of memory is also quite handy, especially in the newer version of the language, such as Java 8.
- Standardized: Java allows programmers, engineers, and scientists to perform standard operations on programs and make use of reusable codes.
- Cross-platform capabilities: Programming languages that can run on any operating system or machine are well-received in the market today, and Java boasts that ability. Users also prefer Java for the absence of need for any special software except JVM.
- Security: Java has a secure system of handling data that defines the access of any class within its ecosystem.
- Multithreaded: Taking advantage of faster systems is another capability of Java, allowing for multithreaded operations or performing multiple tasks simultaneously.
- Performance: Java is heavily reliant on memory, and because of its age, the slower computing speed is noticeable.
- Memory: Whenever a garbage collector is being run in Java, the application’s performance suffers greatly. The performance hit is a significant disadvantage as the other threads in the computing system need to be allocated to the garbage collector thread to work correctly.
Most beginners would often question themselves, “What programming language should I learn for big data” and although the answer may be completely relative to the person, Scala is a great skill to start learning. Object-oriented programming languages are commonly known to have complicated and time-consuming operating capabilities, but Scala breaks the norm with the ideal combination of popular features from other languages.
Apache Spark and Apache Kafka, the two most popular big data framework processors, were built inside the Scala ecosystem if you didn’t already know. A fact like this speaks volumes about Scala’s capabilities and features and should help you decide about the best programming language for big data based on this preference.
- User-friendly: If you’re coming from a Java background and have any experience with object-oriented operations, Scala’s syntax system will feel familiar. Scala is also more compact and concise in its features when compared to Java, making it a direct upgrade if you’re choosing your first programming language for big data.
- Data Analytics: The support that Scala gets from Apache Spark and Apache Kafka makes it a great choice for big data analytics. Its reputation among prominent companies is proof of its analytical capabilities.
- Functional and Complex: Scala lives and breathes for the flexibility of its functions which can be described as a highly capable trait in paradigm. The ability to transfer functions in the form of arguments to other functions is a crucial advantage over other programming languages. You would have to code and debug the whole thing all over again.
- Hybrid Function: Scala’s hybrid nature of being a functional and object-oriented system can prove to be difficult for most beginners to understand.
- Optimization: Scala runs on JVM, which makes it difficult to optimize on tail-recursive situations. However, some people have come up with solutions for this, such as using “@tailrec” annotations for somewhat negligible results.
- Developer Shackles: Scala suffers from limited developer support as it finding Scala developers isn’t as easy as dedicated community pools for Java.
The last one on this list is considered to be one of the fastest-growing and popular programming languages of the last three years. Python is a versatile and generally easy-to-use platform that boasts a wide variety of usage scenarios, with big data management being one of the highlighted areas of application. Some of the most famous libraries used in extensive data programmings, such as NumPy and SciPy, are Python-based.
Python’s ecosystem is so diverse that even machine-learning and deep-learning frameworks such as scikit-learn and TensorFlow make use of its functions and capabilities. The language has found increased usage in big data management because of its broad spectrum of features.
- Library Pool: Python proudly boasts its reputation of having one of the most extensive library pools among programming languages offered for development. Python has tools for literally every kind of operation.
- Versatility: Programming in Python is clear, concise, and allows for flexible configuration with a simple code that allows cooperation between professional work.
- Mobile System Optimization: Python doesn’t operate well with most mobile operating systems components, which makes it weak for optimization as a programming language.
- Risk: Python is a relatively new platform, which means it lacks expert developers’ support and runs the risk of run-time errors with no immediate repair process.
- Browser Integration: Web browsers that are developed in Python consume huge amounts of memory, making it difficult because of the slow nature of Python’s code execution.
Summary: Best Programming Language for Big Data
Extensive data management has always been a challenge for professionals. Still, with the innovation of ideal solutions in versatile and robust programming languages, the struggles are now more manageable. However, the best programming language for big data only exists relative to what kind of usage you want out of it; thus, taking the time to learn and research is key in finding the answer. We hope this post on which programming language to learn for big data was helpful.
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