Python and Big Data – Why they are (almost) perfect friends
Big data is a field that deals with methods for analyzing, routinely extracting information from, or otherwise dealing with data sets that are too large or complex for standard data-processing program applications to handle.
Choosing a programming language for the Big Data environment is highly project-specific and dependent on the project’s goals. And, regardless of the project objectives, Python is the ideal programming language for Big Data due to its ease of readability and statistical processing capabilities.
Python is a rapidly expanding programming language, and a combination of Python and Big Data is the most popular alternative for developers due to less coding and extensive library support.
Here are some of the most common reasons why programmers use Python in dealing with Big Data.
Python programming needs fewer lines of code as compared to other programming languages. It can run programs with just a few lines of code. Furthermore, Python provides automated assistance in identifying and associating data types.
The language is capable of completing lengthy assignments in a brief period of time. Since data processing is not restricted, you can compute data on commodity devices, laptops, clouds, and desktops.
Python is an open-source programming language that was created using a community-based paradigm. Python, as an open-source language, works on a variety of platforms. It can also be run in a variety of environments, including Windows and Linux.
Diverse library support
The majority of Python libraries can be used for data analytics, visualization, numerical computation, and machine learning. And since Big Data needs a lot of computing and data analysis, these Python libraries come in handy.
Python libraries that are most commonly used together with Big Data are pandas, numpy, scikit-learn and scipy.
High compatibility with Hadoop
Hadoop is an open source distributed computing platform that handles data processing and storage in scalable clusters of computer servers for big data applications. Developers prefer Python together with Hadoop because of the PyDoop package.
The PyDoop package offers access to the HDFS API, allowing faster and easier access to directories and files. It also offers the MapReduce API, providing efficient implementation of Big Data concepts such as ‘Record Readers’ and ‘counters’.
Python’s high data processing speed makes it ideal for use in Big Data. Because of its concise syntax and easy-to-manage code, Python codes are executed faster than other programming languages. It supports a variety of prototyping concepts, allowing it to execute code more quickly while retaining excellent clarity between code and execution. Python is consistently one of the most common Big Data options in the tech industry as a result.
Python supports advanced data structures since it is an object-oriented language. It handles a variety of data types, including tables, sets, tuples, dictionaries, and many more.
Its ability to support scientific computations involving matrices and data frames enables it to speed up data operations. This characteristic makes Python very compatible with Big Data.
Big Data is gaining popularity all over the world. It is being used in almost every industry. Thus, application development in this field is also getting more and more extensive. With the incredible features that Python has to offer, it is the perfect fit in providing the computational capability needed in the Big Data platform.
Check out this tutorial to get you started with Python.