July 15, 2024


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Why is Python the most suitable language for Big Data?

Why is Python a Best fit for Big Data? Why is Python a best fit for Big Data

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 is about storing an infinite number of structured or unstructured data on a digital basis, something that would have been almost impossible if we were using the old methods! But you see, that’s not all, Big Data also offers tools to analyze the data and extract practical information from it.

Big Data is an interesting field, yes, but where to 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.

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 loved” language with 73% of developers choosing it above other programming languages prevalent in the market.

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

We can write dozens more lines to convince you that Python is the preferred language for Big Data programmers, but we’d rather take action and list the good reasons why you should love this language.

3. 6 good reasons to associate Python and Big Data

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

3.1. Python is easy to learn

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

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

In Python:

print (‘Hello’)

In Java:

class Hello { public static void main(Strings[] args) { System.out.println(“Hello”) }

Quite a difference, isn’t it? This simplicity of syntax works in your favor when programming Big Data projects. “Do the most with the least” is the motto of this language!  Moreover, 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 both Windows and Linux environments. On top of that, you can port it to other platforms, as it supports many of them.

This means that you won’t have any complications using Python regardless of 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 are composed 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

One of the other reasons why Big Data programmers choose Python to develop their codes is its compatibility with Hadoop. With the Pydoop package (Python and Hadoop), you can access Hadoop’s HDFS API to create MapReduce programs and applications, 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 readers” that make Python programming the best choice for metadata.

3.5. Language Scalability

Language scalability is a criterion to consider when choosing a language when it comes to massive data manipulation. Unlike other Big Data processing languages such as R , Scala or Matlab. Python is the fastest, it is true that it was not always, 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 large family! Generally, complex metadata analysis 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 are sure that Python is your favorite language for Big Data! We’ll introduce you to some small libraries and modules that will come in handy later on.

4. Python, the 5 libraries that make the buzz

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

Among the featured libraries that contribute to Python’s popularity are:

4.1. Tensorflow

Tensorflow is the best-known library in high-performance numerical computation. This library deals with calculations involving tensors and is used in various scientific fields. Among the applications of tensorflow, we find :

  • Image and voice recognition.
  • Video detection.
  • Text-based applications.

This library is mainly characterized by :

  • Parallel computing to execute complex programs.
  • Error reduction with a rate of up to 60% for machine learning problems.
  • Frequent updates and bug fixes.

4.2. Numpy

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

Numerous are the applications of the numpy module, such as :

  • Used by Data Analysts for 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 the object-oriented approach.
  • Table programming oriented for better results.

4.3. Scipy

Here we come to the Scipy library, it is more Data Science oriented. It comes from 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 for solving 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 we find :

  • ETL: process to extract, transform and store data.
  • Data cleaning and visualization.
  • Widely used in customer behavior studies in marketing.

4.5. Matplotlib

Finally we present Matplotlib, or the library of your plotting. It allows you to plot 2D diagrams in order to visualize the results. These plots can be plots, bar graphs, histograms, power spectra, scattering plots or more.

This module has several applications including:

  • Visualization of the correlation between variables .
  • Visualization of the distribution of the data .
  • Visualization of confidence intervals of models 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 that Big Data and Python are the perfect couple! If you are a beginner developer who wants to start Big Data we strongly recommend you to choose this language which will be easier than Java or others. If you are a professional, you already know everything!