As we all know, Big Data is the most valuable commodity in the modern era. The amount of data generated by companies is increasing at a rapid pace. By 2025, IDC says the worldwide data will reach 175 zettabytes. A zettabyte is equivalent to a trillion gigabytes. Now multiply that 175 times. Then imagine how fast data is exploding.
Choosing a programming language for the Big Data field is very project-specific and depends on its goal. And whatever may be the project goals, Python is the perfect programming language for Big Data because of its easy readability and statistical analysis capacity.
Python is a fast-growing programming language, and a combination of Python and Big Data is the most preferred choice for developers due to less coding and tremendous library support.
In this post, let’s explore the benefits of using Python in Big Data and its astonishing growth rate in Big Data Analytics.
1) Simple coding
Python programming involves simple coding compared to other programming languages. We can execute programs with few code lines, and the essential thing is we can associate and identify data types quickly with Python. This language can process and prolix tasks within a short time.
2) Open-source and easy to learn
Python is an open-source programming language developed with the community-based model. It’s free to use, and since it’s open-source supports multiple platforms and can be run on any environment (Linux, Windows, etc.).
Python is easy to learn as well because of its simple syntax. This simple, readable syntax helps Big Data pros to focus on insights managing Big data, rather than wasting time in understanding technical nuances of the language. This one is one of the primary reasons to choose Python for Big Data. Statista states Python remains the most popular programming language in 2020, according to GitHub and Google Trends surveys, surpassing the longstanding Java and Javascript in Popularity.
Also read: We are entering a new age of geospatial Big Data – Dr. Abhay Kimmatkar, Ceinsys
3) Python supports multiple libraries
Python is a famous programming language because of its extensive support for libraries. These libraries are beneficial in saving time and make the language even more popular.
Most of the Python libraries are useful for data analytics, visualization, numerical computing, and machine learning. Big Data requires a lot of scientific computing and data analysis, and the combination of Python with Big Data make them great companions.
Some of the libraries are discussed below:
- Pandas – Free software library to analyze and handle data. Offers multiple data structures to manipulate data. Pandas also support tools for reading and writing data between different data formats and in-memory data structures.
- Numpy – Free software library to compute in arrays and multidimensional matrices. Provides high-level mathematical functions to handle data with random number crunchings, Fourier Transforms, linear algebra, etc.
- Scikit-learn – Free software library for machine learning related to regression, classification, and clustering.
- SciPy – Preferred library for scientific computing and technical computing on data. Allows data integration, interpolation, optimization, and modification using special functions.
4) Python provides high compatibility with Hadoop.
Both Python and Hadoop are open-source big data platforms, and that’s why Python is securely more compatible with Hadoop than any other programming language.
Developers prefer to use Python with Hadoop because of its extensive support for libraries. Also, Python has PyDoop Package, which offers excellent support for Hadoop.
Let’s see what the benefits of using the Pydoop Package are:
- Access to the HDFS API – The HDFS API allows you to read and write information quickly on directories and files without facing any hurdles.
- Offers MapReduce API – PyDoop package offers MapReduce API to solve complex problems with minimal effort. This API allows you to implement advanced data science concepts like ‘Record Readers’ and ‘Counters,’ making Python an excellent fit for Big Data.
5) Python has a high processing speed
Python’s high speed for data processing makes it optimal for usage with Big Data. Python codes are executed in a fraction of the time needed by other programming languages because of its simple syntax and easy-to-manage code. It supports various prototyping ideas, making it run code faster while maintaining excellent transparency between code and execution. This consistently makes Python one of the most popular options for Big Data in the tech industry.
6) Scope
Python is an object-oriented language, which supports advanced data structures. It allows users to imply data structures, including lists, sets, tuples, dictionaries, and many more.
It also supports various scientific computing operations like data frames, matrix operations, etc. These incredible features of Python enhance the language’s scope and thus enable it to simplify and speed up data operations. This is what makes Python and Big Data a deadly combination.
Also read: Geospatial Python: Do you need to learn it?
7) Python has data processing support
Python has an in-built feature of supporting data processing for unconventional and unstructured data, and this is the most common requirement for Big Data to analyze social media data. That’s the reason why big data companies choose Python as an essential requirement in Big Data.
8) Python is portable
This is the most crucial reason why Python is popular in data science. Many cross-language operations are performed easily on Python because of its portable and extensible nature. Many data scientists prefer using graphics processing units for their Machine Learning models, and the portable nature of Python is well-suited for this.
9) Python has large community support.
Big data analysis usually deals with complicated problems that need community support for solutions. Python has large and active community support, which helps data scientists and programmers with expert backing on coding related issues. Also, corporate support is a significant part of the success of Python for Big Data. Top tech companies like Facebook, Instagram, Netflix, etc., use Python in their products.
10) Scalability
Scalability matters a lot when dealing with data. Unlike other languages, Python is much faster. If the data volume is increased, Python easily increases the speed of processing the data, which is tough to do in languages like Java or R.
This makes Python and Big Data fit with each other with a grander scale of flexibility.
Final Words
These were some of the most significant benefits of using Python for Big Data. Big data technology is spreading worldwide, and meeting the demands of the industry is definitely a daunting task. But with the incredible benefits of what Python offers, it has become a perfect option for Big Data. To conclude, Big Data and Python together provide a robust computational capability in big data analysis platforms. I hope by now, you got a clear idea of why Python is considered a perfect fit for Big Data.
Also read: Big data in GIS environment
