Why you should use python for Big Data



Choosing a programming language over another in the big data field is very much project specific and depends on the project goal. However, whatever may be the goal Python and Big Data is an inseparable combination when we consider a programming language for big data development phase.

It is a crucial decision because once you start developing your project in a language, it is difficult to migrate in another language. Moreover, not all big data projects have the same goal. For example, in a big data project, the goal may be simply manipulating data or building analytics while in others it could be for the Internet of Things (IoT).

Furthermore, Python is not limited to big data only and widely used in other technical fields as well which adds its usefulness. IEEE Spectrum has also ranked Python as number one programming language. In this blog, we will discuss on few reasons why Python and Big Data combination is a favorite choice for big data professionals.


Python is a general-purpose programming language which enables programmers to write fewer lines of codes and make it more readable. It has scripting features and besides that uses many advanced libraries such as NumPy, Matplotlib, and SciPy which makes it useful for scientific computing.

Python is an excellent tool and a perfect fit as python big data combination for data analysis for the below reasons:

  • Open source: Python is an open source programming language which is developed using a community-based model. It can be run on Windows and Linux environments. In addition to that, you can port it to other platforms as it supports multiple platforms.
  • Library Support: Python is widely used for scientific computing in both academic and multiple industry fields. Python consists of a large number of well-tested analytics libraries which include packages like
      • Numerical computing
      • Data analysis
      • Statistical analysis
      • Visualization
      • Machine learning

  • Speed: As Python is a high-level language, it has many benefits which accelerate the code development. It enables prototyping ideas which make coding fast while maintaining the great transparency between code and its execution. As a result of the code transparency both the maintenance of the code and the process of adding it to the code base in a multi-user development environment becomes easy.
  • Scope: Python is an object-oriented programming language which also supports advanced data structures such as lists, sets, tuples, dictionaries and many more. It supports many scientific computing operations like matrix operations, data frames, etc. These abilities within the Python enhance the scope to simplify and speed up data operations.
  • Data Processing Support: Python provides advanced support for image and voice data due to its inbuilt features of supporting data processing for unstructured and unconventional data which is a common need in big data when analyzing social media data. This is another reason for making Python and big data useful to each other.

Python is considered as one of the best data science tool for the big data job. Python and big data are the perfect fit when there is a need for integration between data analysis and web apps or statistical code with the production database. With its advanced library supports it helps to implement machine learning algorithms. Hence, in many big data aspects, Python and big data complement each other.

  1. It’s a bag of powerful scientific packages

Python big data combination is backed by its robust library packages which fulfill analytical and data science needs and makes it a popular choice in big data applications.

Some of its popular libraries which make Python and big data useful together those are:

  • Pandas: Pandas is a library which helps in data analysis. Besides that, it provides the required data structure and operations for data manipulation on time series and numerical tables.
  • NumPy: NumPy is the fundamental package of Python which makes possible scientific computing. It provides the support for linear algebra, random number crunching, Fourier transforms. Also, it supports multi-dimensional arrays, matrices with its extensive library of high-level mathematical functions.

  • SciPy: SciPy is a widely used library in Big data for scientific and technical computing. SciPy contains different modules for
      • Optimization
      • Linear algebra
      • Integration
      • Interpolation
      • Special functions
      • FFT
      • Signal and image processing
      • ODE solvers
      • Other tasks common in science and engineering
      • Mlpy

Mlpy is a machine learning library which works on top of NumPy/SciPy. Mlpy provides many machine learning methods for problems and helps to find a reasonable compromise between modularity, reproducibility, maintainability, usability, and efficiency.

  • Matplotlib: Matplotlib is a python library which helps in 2D plotting for hardcopy publication formats with an interactive environment across platforms. Matplotlib allows generating plots, bar charts, histograms, error charts, power spectra, scatter plots, and more.
  • Theano: is a Python library for numerical computation. It allows optimizing, defining and makes it possible to evaluate mathematical expressions which could involve multi-dimensional arrays also.
  • NetworkX:  is a library for studying graphs which helps you to create, manipulate, and study the structure, dynamics, and functions of complex networks.
  • SymPy: is an effective library for symbolic computation which includes features like –
      • Basic symbolic arithmetic
      • Calculus
      • Algebra
      • Discrete mathematics
      • Quantum physics.
      • Computer algebra capabilities in different formats like as a standalone application, or as a library to other applications, or live application on the web.
  • Dask: is a Python big data library which helps in flexible parallel computing for analytic purpose. From the big data perspective, it works with big data collections like data frames, lists, and parallel arrays or with Python iterators for larger than the memory in a distributed environment.
  • Dmelt: Dmelt or DataMelt is a Python-based library or software which is used in big data analysis for numeric computation and statistical analysis of big data and its scientific visualization.
  • Scikit-learn: is a machine learning library which complements NumPy and SciPy libraries. It has various features like –
  • Regression
  • Clustering algorithms for vector machines, gradient boosting, random forests-means and DBSCAN,
  • Interoperates with the Python libraries like NumPy and SciPy.
  • TensorFlow:  is an open source software library supported by Python for machine learning for a range of tasks. The library is capable of building and training neural networks to
      • Detect patterns
      • Decipher patterns
      • Correlations
      • Analogous for the purpose of learning and reasoning.

Python with the libraries mentioned above makes big data scientists’ life easy. For example, with Python library integration with Spark and Scikit-learn data scientists can write code and test with small data sets before it is implemented on Spark cluster. Once the code is verified and works with its desired functionality, they can implement the same on the Spark cluster with a large set of data. This helps to escape them from repetitive code cycles and accelerate business decision.

To use any library, scientists need to search online by tagging ‘Python + [required analytics tool].’ This shows up the testing code with the analytics and required documentation for it along with examples as guidance.

  1. Compatible with Hadoop

As Python big data is compatible, similarly Hadoop and big data are synonymous with each other. Hence, Python has been made inherently compatible with Hadoop to work with big data. Python consists of Pydoop package which helps in accessing HDFS API and also writing Hadoop MapReduce programming. Besides that Pydoop enables MapReduce programming to solve complex big data problems with minimal effort.

  1. Easy to Learn

Python is easy to learn as it abstracts many things with its features. As a result, user needs to code fewer lines of code. Besides that it has scripting feature as well. Python is coupled with user-friendly features like code readability, simple syntax, auto identification and association of data types and easy implementation.

  1. Scalability

Scalability matters a lot when you are dealing with massive data. Unlike other data science languages like R, MatLab or Stata, Python is much faster. Though there was initial complain about its speed, however, with Anaconda its speed performance has enhanced a lot. This makes Python and big data compatible with each other with a greater scale of flexibility.

  1. Large Community Support

Big data analysis often deals with complex problems which need community support for solutions. Python as a language has a large and active community which helps data scientist and programmer with expert support on coding related issues. This is another reason for its popularity.


Python and big data together provide a strong computational capability in big data analysis platform. If you are a first-time big data programmer, no doubt it is easy to learn for you than Java or other similar programming languages. Besides that, if you want to pursue Hortonworks or Cloudera big data certifications, this is a prerequisite to learn either Scala or Python.