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Why Python is the Top Choice for the Big Data Industry

Across most industry verticals, Python is commonly used. It is no surprise that Stack Overflow Trends rates Python as the fastest-growing programming language. It is also the second “most loved” language as per Stack Overflow Developers’ Survey 2019, with 73% of the developers choosing it above other languages being used in the market.

Python is in great demand in the big data industry. This stems from the fact that big data professionals need a language that is easy to use, has good library availability, and sees great community participation. The active community support means the platform is properly maintained and properly updated, something missing from languages with inactive communities.

It is fast!

One big reason for the popularity of Python, as compared to other languages used in software programming, is its performance and speed. It works well with prototyping ideas that help to accelerate code and make it run fast, sustaining transparency while doing so. This also aids in maintaining the code properly, which is why it is a great choice for big data analytics.

There was a time when Python was considered to be slower than some of its competitors such as Java and Scala. However, with the development of the Anaconda platform, the speed has gone up immensely, and big data professionals work well with Python.

It is open-source.

Python is an open-source programming language that relies on a community-based model. As with other open-source developments, Python works well with multiple platforms, and it can also be run in Linux, Windows, and other environments. According to Bram Cohen, the author of the BitTorrent peer-to-peer protocol, “My favorite language for maintainability is Python. It has simple, clean syntax, object encapsulation, good library support, and optional named parameters.”

Coding is simpler.

When compared to other commonly-used languages in coding, Python is much simpler to use, as its programming involves fewer lines of code. A program can be executed with the least lines of code, which helps its case when it comes to the world of big data analytics. Programmers estimate that if working in Java means writing 200 lines of code, the same result could be achieved with a tenth – i.e. just 20 lines – of the code with Python. It also automatically helps in identifying and associating different types of data, and its indentation-based nesting structure lets it process lengthy tasks within a short span of time. You can compute data on the cloud, on laptops, or on desktops!

There is support for data processing.

Unconventional and unstructured data is often a big hurdle in the big data industry. However, Python offers inbuilt support to process data that is unconventional or unstructured, which makes its case for use in big data much stronger.

You can use multiple libraries.

Scientific computing requires the use of more than one library, a feature that Python sports. For just about everything that a big data scientist does on an average day, there is an open-source library available. It thus works well in big data analytics, offering libraries with packages such as data analysis, machine learning, numerical computing, statistical analysis, and visualization.

As of now, estimates suggest more than 70,000 libraries in the Python Package Index, a number that is constantly going up. Among the most popular ones is pandas, an open-source data analysis library with a high-performance set of applications that simplify the task of data analysis.

It is compatible with Hadoop.

Python and Hadoop are both open-source platforms used by the big data industry. When compared to other programming languages, Python offers better compatibility with Hadoop, with the following advantages:

• Access to the HDFS API: The Pydoop package (Pydoop is a Python interface to Hadoop) offers access to the HDFS API for Hadoop, which allows a programmer to write applications and programs using Hadoop MapReduce. This makes it easy to read and write information on files, directories, and global file system properties seamlessly.

• Offers MapReduce API: This helps to solve complex problems with minimal programming efforts. The MapReduce API is useful in implementing ‘Counters’, ‘Record Readers’, and other advanced concepts used by big data professionals.

Python offers an enhanced scope.

Being an object-oriented language, Python offers support for advanced data structures, making it simpler to use in data operations for big data analytics. It helps to manage dictionaries, lists, tuples, sets, and many more types of data structures. It also offers support for data frames, matrix operations, and other operations in scientific computing. This wide-ranging support allows it to speed up the work of big data professionals.

Big data is in widespread use in industries of all types and different sizes, which creates huge demand for programming languages that can simplify the task of analytics. With its huge set of advantages, Python is clearly one of the best choices for the big data industry.


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