Why is Python the most suitable language for Big Data?

1. Introduction

Big Data! This is perhaps one of the words you hear the most these days with the digital revolution, the automation of processes and the remarkable explosion of digital data. Indeed, it’s about storing an infinite amount of structured or unstructured data on a digital basis, something that would have been almost impossible using the old methods! But that’s not all, the Big Data also offers tools for analysing data and extracting practical information from it.

Big Data is an interesting field, yes, but where do we start? The first thing you need to think about before starting Big Data programming is the programming language itself. Python? Java? C ? It must be said that a lot of programmers prefer Python! Of course for several reasons that we will reveal to you later on.

2. Python, the preferred language of Big Data developers

Python is a well-known language developed for object-oriented, functional and imperative programming. It is also very popular in the field of Big Data. According to the Stack Overflow Developers’ Survey 2019, Python is the second “most popular” language with 73% of developers choosing it over other programming languages on the market.

This success is due to the fact that Python offers a variety of features and libraries to explore and transform large data formats. Plus, because of its versatility, Big Data programmers can use it for almost any problem associated with this field!

We can still write dozens of lines to convince you that Python is the preferred language of Big Data programmers, but we prefer to take action and list the good reasons why you will love it.

3. 6 Good reasons to combine Python and Big Data

Python is an excellent tool and a perfect fit as a combination of Big Data and Python for data analysis for the following reasons:

3.1. Python is easy to learn

Python is an easy language to learn because it summarizes many features that would have required several lines of code in another language. Python has other advantages such as code readability, simple syntax, automatic identification, data type association and implementation. Here is a small basic example to demonstrate the simplicity of code in Python :

Here are two programs that both return the same result, the first one in Python and the second one in Java :

  • In python:
    print ('Bonjour')
    • In Java :
      class Bonjour { public static void main(Strings[] args) { System.out.println("Bonjour") } }

Quite a difference, isn’t it? This simplicity of syntax works in your favour when programming Big Data projects. “Do the most with the least” is the motto of this language! In addition, there are hundreds of free tutorials to learn python online.

3.2. Python, a language for everyone

Python is an open source programming language that is developed using a community-based model. It can be run on Windows and Linux environments. In addition to this, you can port it to other platforms, as it supports many of them.

That means you won’t have any complications using Python whatever your operating system or environment!

3.3 Best packages and libraries for Big Data

If Python is ranked among the top programming languages it is also thanks to the strength of its well tested packages and analysis libraries. Indeed, it has a multiplicity of libraries for the different needs of the programmer.

Since Big Data requires a lot of data analysis and scientific calculations, Python and Big Data are the perfect combination! Python libraries consist of packages such as numerical computation, data analysis, statistical analysis, data visualization or machine learning.

For example, the Numpy, Scipy and Pandas modules are used to implement various Big Data operations on a daily basis.

3.4. Compatibility with hadoop , pydoop package

Another reason why Big Data programmers choose Python to develop their code is its compatibility with Hadoop. Thanks to the Pydoop package (Python and Hadoop), you can access the HDFS API of Hadoop to create programs and applications for MapReduce for example.

Pydoop also offers a MapReduce API to solve complex problems with minimal programming effort. This API can be used to implement advanced data science concepts such as “counters” and “record players” that make Python programming the best choice for metadata.

3.5. Evolution of language

The scalability of the language is a criterion to be taken into consideration when choosing it when it comes to massive data manipulation. Unlike other Big Data processing languages such as R , Scala or Matlab. Python is the fastest, it’s true that it hasn’t always been, but with the appearance of Anaconda and the evolution of its performance Python and Big Data have become compatible with each other with greater flexibility!

3.6. Python Community

By joining the Python community, you will be part of a very big family! Generally, the analysis of complex metadata requires the support of the community to find solutions, Python as a programming language has a large and active community that allows different developers to communicate with each other to find solutions to their most complex problems. This is another good reason to choose Python!

Now that we’re sure that Python is your favorite language for Big Data! We’re going to introduce you to some small libraries and modules that will be useful for you later on.

4. Python, the 5 bookstores that make the buzz

Python is a powerful science package fair, the choice of the Python Big Data couple is justified by its robust packages that meet the data science and analytical needs of programs.

Some of the flagship libraries that contribute to the popularity of Python include :

4.1. Tensorflow

Tensorflow is the best known library for high performance computing. This library deals with calculations involving tensors and is used in various scientific fields. Among the applications of tensorflow are :

  • Image and Voice Recognition.
  • Video Detection.
  • Text-based applications.

This bookstore is mainly characterized by :

  • Parallel Computing to Run Complex Programs .
  • Error reduction with a rate of up to 60% for machine learning problems.
  • Updating and fixing very frequent bugs.

4.2. Numpy

The famous Numpy! It is the fundamental module of numerical computation in Python. It allows the processing of high-performance multidimensional arrays of objects. Numpy also manages the problem of slowness by providing features and methods that work efficiently on these arrays.

Multiple applications of the numpy module, such as :

  • Data analysis.
  • Father module of some other libraries like Scipy or matplotlib .
  • Creates powerful N-dimensional tables.
  • Application with Matlab.

The strength of the numpy module is justified by :

  • Fast precompiled functions for basic calculations.
  • Supports object-oriented approach.
  • Table programming oriented for better results.

4.3. Scipy

Here we are at the Scipy bookstore, it’s more Data Science oriented. It’s a descendant of the numpy module. SciPy is a library widely used in Big Data for scientific and technical computing. This library contains different modules for :

  • Optimization.
  • Linear algebra.
  • Interpolation.
  • Image and signal processing.

Scipy is characterized by :

  • Multidimensional image processing tools.
  • Predefined Functions to solve differential equation problems.
  • Advanced features for data manipulation and visualization.

4.4. Pandas

Pandas is an essential module in data processing. It is one of the most popular libraries in Data Science. Indeed, Pandas provides very varied and easy to manipulate data structures. Among the applications of this library are :

  • ETL: process of extracting, transforming and storing data .
  • Data cleansing and visualization.
  • Widely used in studies of customer behaviour in marketing.

4.5. Matplotlib

Finally we present you Matplotlib, or the library of your tracings. It allows you to draw 2D schematics to allow you to visualize the results. These diagrams can be plots, bar graphs, histograms, power spectra, diffusion plots or more.

This module has several applications including :

  • The visualization of the correlation between variables .
  • Visualization of data distribution .
  • Visualization of model confidence intervals up to the 95% level.

5. Conclusion

For all these reasons, which are only a small sample of the power of this language. We think Big Data and Python are the perfect couple! If you are a beginner developer who wants to get started with Big Data we strongly recommend that you choose this language which will be easier than Java or others. If you are a professional, you already know everything!

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