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Big data development needs to learn which programming languages, I believe that many inexperienced people are helpless about this, this article summarizes the causes and solutions of the problem, through this article I hope you can solve this problem.

With the continuous warming of big data, more and more people are engaged in the wave of big data. Many small partners who have no foundation at all will inevitably have such questions. What programming language do you need to learn to engage in big data? In fact, there is no fixed answer to this question, such as Python, R, Java and Scala are good choices, you can choose according to your own actual situation.

1、Python

The average data scientist will choose Python as the first choice of big data language. Python has long been popular in academia, especially in fields such as natural language processing (NLP). So when there's a project that needs NLP, you're faced with a dizzying number of choices, including classic NTLK, theme modeling using GenSim, or ultra-fast, accurate spaCy. Not only that, Python is also applicable in the field of neural networks. Python is usually supported in big data processing frameworks.

Python, in contrast to R, is a traditional object-oriented language, so most developers will be comfortable with it. But first contact with R or Scala can be daunting. One small problem is that you need to leave the right white space in your code. This divides people into two camps, those who think "this is very helpful for readability," and those who think we shouldn't have to force the interpreter to make the program run just because a character in a line of code isn't in the right place.

2、R

R is also known as "a language developed by statisticians for statisticians." If you need to compute esoteric statistical models, you may find them on CRAN. When it comes to analysis and plotting, nothing beats ggplot2. And if you want to take advantage of more than what your machine offers, you can use SparkR bindings to run Spark on R.

However, if you are not a data scientist and have not previously used Matlab, SAS, or OCTAVE, it may take some tweaking to use R efficiently. Even though R is good for data analysis work, it is not good for general use. You can build your model in R, but you need to consider converting your model to Scala or Python for production use.

3、Scala

Scala is probably the easiest language to work with because of its type system. Scala runs on the JVM and basically successfully combines functional and object-oriented paradigms. It is making great strides in the financial world and in companies that need to process huge amounts of data. Often in a massively distributed way. It is also a language that drives Spark and Kafka.

Because Scala runs inside the JVM, it has immediate and free access to the Java ecosystem, but it also has a wide range of "native" libraries for handling large-scale data (notably Twitter's Algebird and Summingbird). It also includes a very handy REPL for interactive development and analysis, just like Python and R. Scala has its drawbacks, however, as its compiler runs a bit slowly. However, it has REPL, support for big data, and a web-based notebook framework in the form of Jupyter and Zeppelin. Given all these advantages, Scala's advantages outweigh its disadvantages.

4、Java

Java is suitable for big data projects. Hadoop MapReduce, for example, is written in Java. HDFS is also written in Java. Even Storm, Kafka, and Spark can run on the JVM, which means Java is the preferred programming language for these projects. There are also new technologies like Google Cloud Dataflow, which has only supported Java until now. Developers struggle to untangle a set of callbacks in Node.js applications, access to a vast ecosystem using Java, and much more.

Java's only drawback is that it's cumbersome and lacks the REPL needed for interactive development. R, Python, and Scala all have it. However, the new Lambda support feature in Java 8 will help improve this situation. Java will never be as compact as Scala, but Java 8 does make developing in Java less painful.

After reading the above content, do you know what programming languages you need to learn for big data development? If you still want to learn more skills or want to know more related content, welcome to pay attention to the industry information channel, thank you for reading!

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