Python for Big Data: Top 12 Convincing Reasons To Choose Python for Big Data

What is Python?

Python is a programming language that is most widely used in Data Science, Machine Learning, Deep Learning, and Artificial Intelligence. It is one of the leading programming languages in Big Data Analysis. It is a general-purpose and interpreted programming language which helps develop advanced mobile applications, websites, web applications, and desktop applications. 

Guido Van Rossum invented the python language. Initially, it was created to eliminate flaws in farmer programming language ABC which were developed by Centrum Wiskunde & Informatica (CWI) in the Netherlands. One of the applications of Python is Rapid Application Development which uses various specialities such as dynamic binding and dynamic typing.

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Why Python for Big Data?

There are many types of applications that can be used to built by Python programming language. But Python offers better ease of access, time efficiency, better results, better benefits, and involvement. There are many benefits from Python Language, which are more than other languages like Java, R and many more.

Python helps in meeting the goal of the project within time and no hurdles. The best part of Python is it can be easily migrated into any desired programming language of any data science or big data projects at any time. This brings higher efficiency by Python for any project in a company.

For Artificial Intelligence, the Internet of Things and many more, Python has become one of the most suitable programming languages as pointed out by experts and many developers. It helps businesses a lot in completing the goal of a project on time and also favours the developers at the same time.

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The Benefit of Python in Big Data

There are many more reasons and benefits from Python that we are going to discuss here:

1. Data Visualization

There are many visualization packages in Python programming language when compared with other programming languages. In this case, Python easily beats its competitor programming language R. NetworkX, Pyga, Matplotit, Plotly are some of the visualization packages in the Python programming language. Read: Python vs R

2. Unlimited Data Processing

Developers are free to load high data volume for data processing through python packages, and it does not limit the processing of data.

3. Large Community Support

There is a large community of data experts and developers where issues are solved in real-time with the help and knowledge shared by each other.

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4. Scalability

Python is the best programming language when it comes to scalability. It can quickly increase the processing speed of data whenever the count of data is increased. Other programming languages such as Java or R are unable to scale like Python programming language. Other programming languages are not able to handle the large volume of data. On the other hand, Python programming language is very smooth and easy to handle a massive amount of data.

5. Flexibility

Python programming language is also one of the most flexible languages. One can easily create a backup of the MySQL database by merely downloading it.

6. Ease of learning

Python programming language can quickly be learned because a non-programmer can also skim the syntax of Python. There is no need to be a programmer or developer to learn or understand the Python language. The support for python programming language on time from the large community helps in solving many live issues. One can also quickly learn Python by using Python in real-world applications.

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7. High Compatibility with Hadoop

One of the main reasons to choose Python for Big Data is that it can create secure inherent capability between Big Data and Hadoop. There are packages in Python such as PyDoop Package which provides excellent support to Hadoop.

Hadoop can write Hadoop MapReduce applications and programs using the HDFS API from PyDoop Package. It is also easy to access, write and read the file from global file systems or directories using HDFS API. Much lesser effort in programming is needed to solve a complicated issue by using MapReduce API of Hadoop.

8. Many Powerful Scientific Library Packages

There are many scientific library packages in the Python library which are best for Big Data Processing. Let us check out some of the most important libraries in Python:

  • SciPy

This python library package is used for technical and scientific computation. There are many kinds of modules for data engineering tasks and data science such as FFT, ODE solvers, Signals & Image Processing, Interpolation, and Linear Algebra.

  • NumPy

The original package for scientific computing on data is NumPy. There are many things which are supported by NumPy such as easy integration with different databases, supporting a multi-dimensional array of generic data, random number crunching, Fourier transforms, linear algebra and many more.

  • Pandas

Pandas python library is used in data analysis. There are many different kinds of operations done using Pandas, such as manipulation of data. Manipulation of Data can be operated on numeric tables and time series tables. There are also some functions in this library which helps to deal with the different structures of data.

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9. Programming Scope

There are many kinds of concepts in a data structure such as Data Frames, Matrix, Dictionaries, Tuples, Sets, Linked Lists and many more which are supported by the Python programming language. Python can support all these data structures because it comes under the concept of Object-Oriented Programming (OOP).

10. Platforms Scope

Development of mobile app development, website development, web applications, data processing applications, graphic user interface application and many more are easily supported by the Python programming language. It is because the Python programming language is a general-purpose language.

11. Support for Data Processing

Python is very supportive in terms of processing the data and primarily to handle unstructured data. It is also beneficial when it comes to process data from social media because it contains Image data, text data, and voice data. All the unstructured data from social media is quickly processed using an inbuilt feature in Python to identify the type of data.

12. Ultra Data Processing Speed

There is an expectation of fast data processing by any developer to write and execute the codes. In Python, It has a characteristic which provides ultra processing speed to process the data. The data codes are executed in a fraction of time because the programs are written in simple codes of python programming language.

13. Lesser Codes

The best part of python programming language would be that it can easily be used to develop applications and programs with just a few lines of coding. The Python has good increased readability because it follows nest structure. It also can identify the types of data automatically due to its inbuilt features.

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Big Data is the field of computer science which requires a lot of data processing, manipulation, visualisation etc. Python is the best-known programming language to handle problems in the Big Data space. We hope this article has been informative to you and has it clear about Big Data and why Python is best suited for it.

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What exactly is Python?

Python is one of the most widely used programming languages, and it competes with other prominent programming languages such as HTML, Java, and C++ for the top spot. Now that Python is so well-known, you can type an issue into Google and obtain a solution in seconds. It is a strong, flexible, and user-friendly programming language. Python is a high-level interpreted language that is well-suited to building Python scripts for automation and code reuse. It can write functional codes and is also a project-oriented programming language. Even Python is not adequate for a low-level system with hardware interactions.

Why is Python important in Big Data?

Python is useful for Big Data because it manages the best programming language and is also easy to read. Python, too, has ease of programming. It manages the data of Mozilla, Google, and numerous other search engines. It is easily adaptable, and it can even process data at high speeds, which Java cannot. It handles unstructured data such as videos, sounds, and images, and the format of each file varies. When Big Data has a complex problem that requires a solution, Python steps in as an active community and connects data scientists with coding experts.

Why is Python used as a tool in data science?

Python has been used as a data science tool because its syntax is simple and it is easy for people to learn because it does not require an engineering background. It has distinct languages and provides excellent growth opportunities for data scientists. For example, if you learn Python, you can work as a product developer, Python manager, educator, and so on. Python is the foundation for data scientists, allowing anyone to create amazing products that benefit businesses.

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