How Is Python a Perfect Fit for Big Data?

3 shares, 832 points

Big Data is an evolving term describing the volume of a company’s rich data that deluges a business on a day-to-day basis and has the potential to be mined for information. Big Data assists better decision-making and strategic business moves.

Choosing a programming language to best utilize the big benefits of Big Data is a project-specific task which depends on the project objectives. The choice of a programming language merely depends on the project requirements and individual use cases. Well, it is a very crucial decision, because if the project development is started in one programming language, it is not feasible to migrate it to another. For Big Data development, Python is always considered as the ideal programming language. In this blog, we shall understand why enterprises and developers today prefer Python language for Big Data over other languages.

Intellipaat is the most renowned e-learning platform. Intellipaat’s Data Science Course and Python Certification are among the most widespread online courses.

Python Is Emerging as a Leader

There is a battle among many programming languages for becoming the best one, as there is a close combat between Python and R language. Although, Python is emerging as a leader.

How Do Top Industry Players Use Python? The tech giants are using Python as the core language for numerous purposes. Let’s see how.

Google is using Python as one of the three core languages. Components of Google Search engine and Google Spider are written in Python.

Instagram is a social platform where every day over 95 million photos and videos are shared by 400 million active users. Instagram has recently moved to Python in combination with Django which is again a Python-based framework. Engineers at Instagram believe that Python is simple and focuses mainly on user-facing features. 

Amazon analyses its customers’ buying and searching habits, in order to provide proper recommendations to them. They use Python Machine Learning engine to integrate with their vast database.

Facebook uses Python for processing a large number of images on their site. They have tons of images coming up every day. So, they decided to use Python for the back-end applications which is connected to image processing.

Python and Big Data: The Perfect Blend It can be safely said that Python and Big Data make a perfect combination. Python comprises certain advanced libraries like SciPy, NumPy, and Matplotlib making Python the best used tool for scientific computing purposes. Let’s find out more justifying reasons:


Python supports advanced data objects such as sets, lists, dictionaries, tuples, etc. It supports scientific computing operations such as data frames matrix operations, etc. Such capabilities in Python enhance its scope.

Open Source

Python is an open-source language which can be run on both Linux and Windows environments. Python is developed in a community-based model. It also supports multiple platforms: hence, it can be easily ported to another platform.


By definition, Python is known as a high-level programming language, which obviously means that Python has certain characteristics to accelerate the code development procedure. It allows prototyping ideas that fasten the coding and provide good transparency between the code and its execution. This transparency simplifies the tasks like code maintenance and transferring the code to the code base in a multi-user system.

Library Support

Python is popularly used for scientific computing. It comprises many analytical libraries. The library features are as below:

  • They support multi-dimensional arrays.
  • They support array processing.
  • The operations are conducted element-wise.
  • They have features of mathematical operations.

The analytical libraries which Python has are as below:

  • Data analysis
  • Statistical analysis
  • Numerical computing
  • Machine Learning
  • Visualization

Data Processing Support

Python gets well integrated with voice and image data. This is because of its inherent feature to support data processing of unconventional and unstructured data. It’s a very common requirement of Big Data while analyzing data coming from social media. This is how Python and Big Data are useful for each other.

Why Does Python Become a Perfect Fit for Big Data?

Whenever there is a requirement in Big Data for integration between web applications and data analysis or production database with statistical code, Python will be considered first over any other programming languages. Big Data and Python complement each other in the following ways.

Python Is a Complete Package

Python is a powerful package which fulfills a wide range of Data Science and Data Analytics requirements. Some of the package inclusions are:

SciPy: Meant for technical and scientific computing, SciPy has various modules such as:

  • Linear algebra
  • Optimization
  • Integration
  • Special functions
  • Interpolation
  • Signal and image processing
  • FFT
  • ODE solvers

Pandas: This library assists data analysis. It also gives a wide range of functions dealing with data structures and operations such as manipulation on numerical tables and time series.

NumPy: This library is a significant part of Python and scientific computing. It assists matrices with high-level mathematical functions. It supports multi-dimensional arrays, and it can easily integrate in an environment with multiple databases. It also supports linear algebra, Fourier transforms, random number crunching, etc.

Mlpy: This is a Machine Learning library working ahead of SciPy or NumPy. Mlpy resolves certain Machine Learning-related issues such as getting a reasonable compromise among reproducibility, modularity, maintainability, efficiency, and usability.

Matplotlib: This Python library helps 2D plotting used for hardcopy publication formats which have an interactive environment across various platforms. It enables several features like generating plots, histograms, bar charts, error charts, scatter plots, power spectra, etc.

Theano: It is a Python library specifically designed for numerical computing purposes. It helps in defining and optimizing, and it evaluates mathematical expressions comprising multi-dimensional arrays.

NetworkX: It is used for studying graphs and helps generate, operate, and study:

  • Dynamics
  • Structure
  • Functions of complex networks

SymPy: This Python library is used for symbolic computation including features such as:

  • Basic symbolic arithmetic
  • Algebra
  • Calculus
  • Quantum physics
  • Discrete mathematics
  • Computer algebra capabilities in several formats

Scikit-learn: This is another Machine Learning library complementing SciPy and NumPy. Its features are:

  • Clustering algorithms for gradient boosting, vector machines, DBSCAN, and random forests means
  • Regression
  • TensorFlow

TensorFlow: It is an open-source software library which is supported by Python for Machine Learning tasks. It is capable of building neural networks for:

  • Decoding patterns
  • Detecting patterns
  • Finding correlations
  • Learning and reasoning

Python’s Compatibility with Hadoop

By now, it is quite evident that Python and Big Data go very well with each other. In the same way, Hadoop and Big Data are synonymous. In order to mix well with this combination, Python is already inherently designed to become compatible with Big Data and Hadoop. Python has the Pydoop package which can access HDFS API. It can also write Hadoop MapReduce programming. Pydoop can solve complex problems related to Big Data with minimal effort.

Ease of Learning

As compared to other programming languages, Python is very easy to learn. Even non-programmers consider Python as the best and easy-to-learn language. Python is preferred by beginners for its simple features. Primary reasons for why beginners opt Python are its readable code, ample learning resources, simple syntax, large community, auto identification, and easy implementation.

Data Visualization

Python inherently has a wide range of visualization features. It has recently enhanced its Data Visualization package. Matplotlib has laid the foundation on visualization, based on which various libraries are created such as Seaborn, ggplot, pandas plotting, etc. This assists you to create charts, web-ready interactive plots, and graphical plots. Python enables you to use TabPy for Tableau integration, and you can also use win32com and Pythoncom for integration with QlikView. Both of these are visualization tools for Big Data.


When there is a massive data involved, scalability has high importance. As mentioned earlier, compared to other languages, Python is much faster and scalable. With the recent versions of Python, the speed has been further enhanced.

Large Community Support

Very often, Big Data Analytics deals with complex problems which need community support. This means that, if you are struck in any phase of development using Python, the community support staff from Python will assist you to solve your issues. The support they provide is quite quick and helpful. It has a very large and active community support which helps the Data Scientists and Programmers across the globe with expert solutions.

Evidently, we can conclude that Python and Big Data together form a strong computational capability in the analytics stream. Besides the Python training course, at Intellipaat you will find enormous industry-recognized training courses which can help you fast-track your career.

Author Bio:

Sonal Maheshwari

Sonal Maheshwari has 6 years of corporate experience in various technology platforms such as Big Data,

Data Science, Salesforce, Digital Marketing, CRM, SQL, JAVA, Oracle, etc. She has worked for MNCs like

Wenger & Watson Inc, CMC LIMITED, EXL Services Ltd., and Cognizant. She is a technology nerd and

loves contributing to various open platforms through blogging. She is currently in association with a

leading professional training provider, Intellipaat Software Solutions and strives to provide knowledge to

aspirants and professionals through personal blogs, research, and innovative ideas.


Your email address will not be published. Required fields are marked *