Why Python Is Essential for Big Data and Data Science ?

Its producers define the Python language as “…an interpreted, an object-oriented, high-level programming language with dynamic semantics. It’s high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components.” Python is a general-purpose programming language, meaning it can be used in the development of both web and desktop applications. It’s also useful in the development of complex numeric and scientific applications. With this sort of versatility, it comes as no surprise that Python is one of the fastest-growing programming languages in the world.

So how does Python jibe with data analysis? We will be taking a close look as to why this versatile programming language is a must for anyone who wants a career in data analysis today or is looking for some likely avenues of upskilling. Once you’re done, you’ll have a better idea as to why you should choose Python for Big data analysis.

Python for Big Data

Typically, Python’s simple syntax and gradual learning curve has been one of the most popular reasons as to why it’s used in Big data . It would be interesting to know that interns in organizations are actively engaged in teaching the language to new employees. To get in-depth knowledge on Python along with its various applications, you can enroll for live Python training with 24/7 support and lifetime access. Python allows organizations to move code from development to production more quickly since the same code made as a prototype can be moved into production.

We all know that Hadoop is an important technology that has gained huge popularity as a Big data Solution, but did you know that Python is used to write Hadoop’s MapReduce programs and applications to access HDFS API for Hadoop with PyDoop Packages?

Let us look at PyDoop, an application package that provides a Python API for Hadoop’s MapReduce and HDFS. Perhaps one of the most important link between Python and Big data , the PyDoop has several advantages over Hadoop’s built-in solutions for Python programming which includes Hadoop Streaming.

The biggest advantage of PyDoop is it’s HDFS API. This allows one to connect to an HDFS installation, read and write files , and get information on files, directories and global file system properties.

The MapReduce API of PyDoop allows one to solve many complex problems with minimal programming efforts. Advance MapReduce concepts such as ‘Counters’ and ‘Record Readers’ can be implemented in Python using PyDoop.

As per the job trends on Indeed.com, Python and R combination with Big data is picking up steadily. With many companies looking for Big data analytics, Python training seems to be a must on your resume. Python is by far the most in demand for jobs in the Big data field. Python for Big data Training automatically qualifies you for those jobs.Completing the Python training helps you in finding high-paying jobs within a short time. With many more jobs coming up in Big data , Python training will make you the ideal candidate.

Python is Key to the Future of Data Science

If you read through the latest edition of GitHub’s State of the Octoverse—a comprehensive report on the code respository’s biggest trends—you might pick up on something interesting. Although it’s already a well-established programming language, Python is continuing to grow at the rate of an up-and-coming one, gaining 151 percent in usage since 2018.

Part of that continued rise is directly attributable to data science , GitHub added in its report. “Behind Python’s growth is a speedily-expanding community of data science professionals and hobbyists—and the tools and frameworks they use every day,” it stated. “These include the many core data science packages powered by Python that are both lowering the barriers to data science work and proving foundational to projects in academia and companies alike.”

Indeed, data science and machine learning repositories on GitHub have enjoyed extreme growth. Developers (and the companies they work for) clearly feel that analytics and machine learning are the keys to the future, and Python is playing a significant role in that. “Among the most popular (based on star counts) public repositories labeled with the topic, over half of them are built on numpy, and many of them depend on scipy, scikit-learn, and TensorFlow,” GitHub added. “We’ve also seen non-code contributions from the data science field, including academic papers.”

Despite its simplicity, Python is vastly powerful for solving complex and difficult problems in virtually any domain. Python is platform independent, and so it can integrate with most existing IT environments. Python has high capabilities for Big Data manipulative tasks and its natural strength as a scripting language makes it highly adaptive for data–oriented applications. No wonder, companies of all sizes and different industry types are using Python to manage their requirements. As companies continue to leverage the power of Python for Big data processing, Python training will help establish your skills in Big data analytics as well as data science .

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