This term becomes viral in the big data industry. There are several programming languages and big data tools to analyze the raw data with different tactics. But, why python is creating hype in data analysis? This is what we gonna see in this article. Here we are going to cover the usage of Python in big data, in different verticals, Like why choose Python for big data projects, what are the top 13 reasons to choose Python for big data analysis, the benefits of using Python for data analysis and data science, etc.
Before starting to explain why use Python for big data, Let us have a short intro to Python.
What is Python?
Python – As by its definition, it is an interpreted and general-purpose programming language. So using Python we can develop advanced desktop applications, web applications, websites, mobile apps, and more. Mr. Guido Van Rossum invented Python to overcome the flaws of the farmer programming language ABC developed by CWI(Centrum Wiskunde & Informatica), Netherlands. Python has several specialties like dynamic typing, and dynamic binding in order to proceed with Rapid Application Development.
Python can be used to develop any kind of application. But, in the big data industry, python provides better involvement, benefits, results, time efficiency, and ease of access than any other languages like R, Java, and more.
Why Python for big data?
Choosing Python in big data is highly project specific, and meets the project goals on time with no big huddles. The most unavoidable risk of big data is industry, “migrating the entire project to another language”. Python brings higher efficiency and provides us an option to easily migrate any big data or data science projects into the desired programming language at any time. Many developers and experts point out that Python is the most suitable programming language for technology projects like AI, IOT and more. Python is not only favoring the developers alone but also favors business in terms of fulfilling the project goals on time. Likewise, we can list out N number of powerful use cases and benefits of Python in big data. Let us discuss the top 13 benefits while using Python for big data in detail below.
13 Reasons To Choose Python For Big Data Projects
- Open sources
- Multiple Library support
- Unbelievable speed of processing
- Scope in Various Platforms
- data processing support
- Powerful Packages
- Lesser codes
- Increased Compatibility with Hadoop
- Easy to Learn
- Flexibility and Scalability
- Support from a large community
- Data Visualization
- Dynamic data processing
Let us discuss all 13 benefits in detail below.
1. Open Source Language
Python is a completely open-source programming language that has been developed as a community-based model, so the developers are connected under one roof. Python can be run on various platforms including Windows, Linux, and more. Since it supports various platforms, we can easily interchange it to any platform at any time. You can download the recent version of Python directly from their official website python.org.
2. Multiple Library Support
Python is widely used in computing in various industry fields, so in order to fulfill the computing process python has been inbuilt with various analytics libraries and packages including:
- Numerical computing Packages.
- Data Analysis Packages.
- Statistical Analysis of Libraries Packages.
- Visualization Packages.
- Machine Learning Packages.
3. Lesser codes
The beauty of Python is we can make programs and applications with the least line of code. Python has been made with an inbuilt nature of automatically identifying data types and follows nesting structures to increase readability. Python can make a program in just 20 lines, whereas in Java, we used to write 200 lines. So the development drastically decreases while using Python for big data.
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4. Unbelievable speed of processing
Every developer should expect a programming language to be faster while writing and executing the codes. Python meets developer expectations with ultra-speed data processing characteristics. As Python makes a program in simple codes, it increases the execution of data in a fraction of the time.
The acceleration of code development has been fulfilled as it enables prototyping ideas during the code writing which makes the execution of codes faster. The transparency between code and its execution makes code maintenance easy in a multi-user development environment.
5. Data Processing Support
Python provides increased support for big data analytics to identify and process unstructured data. Python has an inbuilt feature of identifying voice, text and image data so it can be very useful in big data analytics while processing social media data.
Scope in programming: Pythons come under OOP’s Concept, which is created to support various data structure concepts like Linked Lists, sets, tuples, dictionaries, Matrix, data frames and more. This is also another factor of increased data processing.
Scope in platforms: As said earlier, Python is a general-purpose language, so it supports the development of various GUI applications, Data processing applications, web applications, website development, and mobile app development.
7. Powerful Scientific Packages
Python is the best fit for big data, as it has many robust scientific library packages. Let us have a look at some of those library packages
It helps in data analysis. Provides various operations like data manipulation on time series and numeric tables also some functions to deal with different data structures
NumPy is the primary package of Python which is scientific computing on data. It supports linear algebra, Fourier transforms, and random number crunchings. Also, support a multi-dimensional array of generic data to easily integrate with many different databases.
Used for scientific and technical computing. It contains various modules for data science and data engineering tasks like:
- linear algebra,
- signals and image processing,
- ODE solvers,
and other tasks common in data science and data engineering.
Mlpy– It is a machine-learning library that runs on top of both NumPy and SciPy.
Scikit-learn: Also a machine learning library runs on NumPy and SciPy.
Sympy – Libray for symbolic computation
Thenao – Library for numerical computation
Tensor flow – An open-source software library based on machine learning which is capable of building and also manipulating neural networks.
Tensor flow is used to detect patterns, and decipher the patterns and correlations.
these are the primary libraries that are packed with Python, other libraries are
8. Increased Compatibility with Hadoop
As Python is closer to big data than Hadoop, it creates an easy inherent capability between Hadoop and big data. This is another reason to prefer Python over other languages. Python has PyDoop Package which provides HDFS API for Hadoop in order to write Hadoop MapReduce Programs and applications. HDFS API can be used to connect a program with HDFS installation, hence it makes it easy to read, write, and access files from directories or global filesystems. The MapReduce API of Hadoop can be used to solve a complex problem with lesser programming efforts.
9. Easy to Learn
To learn Python you don’t have to be techies or a programmer. The syntax of Python can be easily readable by non-programmers, and also there is a big developers community to support on time to rectify the lively facing issues. This gives a gradual understanding of learning Python with real-world applications too.
10. Flexibility and Scalability
Python meets the flexibility and scalability while handling a large volume of data, which other languages like R and Java fail to do. Whenever the data count increases python simultaneously can increase the speed of processing the data. it is flexible to download and back up MySQL database.
11. Support from a large community
Python has a large community of developers and data experts which helps them to share their knowledge with each other and provide solutions for live issues on time.
12. No Limitation on data
Python has no limitation on processing the data. So, it provides open freedom for developers to load a huge volume of data, and process it through Python packages.
13. Data Visualization
Python has a variety of visualization packages than any other language, which makes it stand alone from its competitor language R. Visualization packages supported by Python are Plotly, Matploltit, Pyga, NetworkX and more.