In the big data industry, selecting one programming language over another is highly project-specific and is based on the project’s objectives. But regardless of the end goal, when choosing a programming language for the big data development phase, Python and Big Data go hand in hand. With the help of the general-purpose programming language Python, programmers may produce more understandable, concise code with fewer lines of code. It is helpful for scientific computing since it uses NumPy, Matplotlib, and SciPy, among other cutting-edge libraries, and because it offers scripting tools. Python is a great tool and a great fit for python big data combo for data analysis.
One of the top data science tools for large data projects is Python. When it comes to integrating statistical code with the production database or data analysis with web apps, Python and big data are the ideal combination. It aids in the implementation of machine learning algorithms thanks to its sophisticated library features. As a result, Python and big data complement each other in many ways.

Why Python and Big Data Work so Well Together:

The Python big data combo is supported by its powerful library packages, which meet the needs of analytical and data science applications and make it a popular choice.
Python has the Pydoop package, which facilitates building Hadoop MapReduce code and gaining access to the HDFS API. Additionally, Pydoop makes it possible for MapReduce programming to quickly and efficiently address challenging big data problems. Because it encapsulates many concepts with its features, Python is simple to learn. As a result, the user must write less code. Additionally, it offers a scripting feature.
When working with huge amounts of data, scalability is quite important. Python is a lot quicker than other data science languages like R, MatLab, or Stata. Although there were at first complaints about its speed, Anaconda has greatly improved its speed performance. Big data analysis frequently addresses difficult issues requiring cooperation from the community to find solutions. Python has a sizable and vibrant community that provides data scientists and programmers with knowledgeable assistance on coding-related concerns. Another justification for its appeal is this.

Conclusion:

To sum up, Python and big data work well together to give the platform for big data analysis excellent computing capabilities. Big data programming is undoubtedly simpler to master for beginners than Java or other comparable programming languages. In addition, learning Python or Scala is a need if you want to pursue big data certifications from Hortonworks or Cloudera.