The Connection Between Big data & Python

Big Data Python

The selection of any programming language is such a crucial task when we plan to get a software development or application developed. This is due to the fact that there is a specific language over which the performance and the success of that particular software is based.

Among various programing languages, here we will talk about one of the most trendy languages that is Python. Python is indeed one of the most famous programming languages which has been considered to be the most suitable language for Machine learning development because its coding structure is easy to understand.

The simplicity of Python is what makes it so popular!

Let’s look at some of its FEATURES:

The Connection Between Big data & Python 1

Here in this blog, we will share some words about the connection between Big data and Python. Before diving too much into the exact difference, let’s know little about what Big Data is.

What is Big data?

Big data as the name suggests, is basically the analysis of big data by discovering hidden patterns or extracting information from it. So it is basically a huge information analytic where the analysis is completed on huge information.

Big data is known as the process in which we collect and analyze the large volume of data sets (called Big Data) which helps in discovering useful hidden patterns and other information such as customer choices, market trends, etc. which is really beneficial for the organizations to remain informed and taking customer-oriented business decisions.

Big data takes care of the extreme volume of data, different types of data types and the velocity at which the data must be processed. Big data helps in taking better business decisions as well as the future steps that should be taken by the organization.

Why Python is best for Big data?

The reason behind why Python is the best language for Big Data is that Python has a lots of libraries to work on big Data. Python is much better in comparison to other languages in terms of developing code for Big Data because Python is having shorter codes that can be managed easily. These two aspects are enabling developers worldwide to embrace Python as the language of choice for Big Data projects.

Moreover, it is very easy to manage any data type in Python. Let’s prove this by providing a simple example.

In the below image where you can see that the data type of ‘a’ is string and the datatype of ‘b’ is an integer. Here the advantage is that there is no need to manage the data type. It is already managed by Python.

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I would like to recommend Python for Big data because if we talk about other languages like Java, if you write 200 lines of code, the same thing can be done in just 20 lines of code with Python. According to various developers, Java is much better than Python, but according to my personal experience and observations, I can say that Python is much better for Big Data because when you are working with a huge amount of data, the performance could be seen the same with Python as well as with java but Python is more time saving.

From this above-given brief description, it could be said that Python is an excellent tool and a perfect fit as python big data combination for data analysis for the following reasons:

Less is More

Python is able to to make programs work in the least lines of code. It automatically helps identifying and associating data types and follows an indentation based nesting structure. In  a nutshell, it could be better to say that the language is too easy to complete the process within a shorter span of time. You can compute data in commodity machines, laptop, cloud, desktop, basically everywhere as there is  no limitation to the data processing.

There was a time when Python was considered to be a slower language in comparison to some of its counterparts like Java and Scala but with Anaconda platform it has caught up to speed. Since then it became one of the most simple and famous languages for software development.


Python is known as an open-source programming language which is developed with the help of using a community-based model. As python is an open source language so it is able to support multiple platforms and apart from this, it can be run on Windows and Linux environments.

Library Support

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Python is a famous programming language in fields like scientific computing in both academic and multiple industry. There are a large number of well-tested analytics libraries which consists of packages like

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

Python’s Compatibility with Hadoop

The reason why Python is compatible with Hadoop is that both Hadoop and Python are open-source big data platforms. So, this is the main reason why Python is more compatible with Hadoop instead of other languages. Combining these both, the Pydoop package provides you an access to the HDFS API for Hadoop and hence allows to write Hadoop MapReduce programs and applications.

In order to connect your program to an HDFS installation, you can make use of HDFS API, so it will become easy and possible to read, write and obtain information on files, directories, and global file system properties.

In order to solve complex problems with minimal programming efforts, Pydoop offers MapReduce API thus helping to solve all your issues. This API can be used to seamlessly apply advanced data science concepts like ‘Counters’ and ‘Record Readers’.


Talking about the speed and the performance of the Python, it could be said that Python is a high-level programming language so it has been considered to be one of the most suitable language for the software development as it accelerates the code really well. As it supports prototyping ideas which is really helpful for making coding fast and understanding while keeping in mind the transparency between code and the process.

As the code is readable and transparent, this helps in the maintenance of the code and the process and finally, this all makes the development environment so easy.


Python supports advanced data structures as it is an object-oriented programming language. The things which are being managed by Python are  lists, sets, tuples, dictionaries and many more. Apart from this, it helps in supporting many scientific computing operations like matrix operations, data frames, etc. All these specifications and abilities of Python helps in enhancing the scope in order to simplify and speed up data operations.

Data Processing Support

With the help of Python language, you can get support for image and voice data because it is having an inbuilt feature of supporting data processing for unstructured and unconventional data. This could be considered one of the major requirements in big data while talking about the social media data.

Final words

In a nutshell, it is true that Python is one of the simplest languages having simple and readable codes which makes it most suitable for Big Data analysis.

The above-mentioned points are properly highlighting the benefits of Python for Big Data as well as they are also mentioning the strong bond between The Big Data and the Python. So, keeping in mind these facts, it is suggested here that if you want to enjoy the benefits of Python with Big Data, then hire python developers from good python web development companies who would be able to understand your needs.

Without stretching much time, take a further step to do good big data analysis with the help of Python.

Written by Varun Bhagat

Varun Bhagat is a tech consultant & blogger working for PixelCrayons which is a leading software outsourcing company in India. He loves to share his tech knowledge gained over a period of 10+ yrs with like-minded people.

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