Last updated on September 20th, 2021 at 07:36 am
Perks of using Python
A lot of unstructured data is produced each day, the companies and firms use big data and its applications to extract meaningful information from the raw data. A distributed file system is used for parallel processing of data and to enhance fault tolerance. The Hadoop ecosystem offers a Hadoop distributed file system (HDFS) which is widely used by companies and firms.
Hadoop is a database framework that allows users to process big data. While the Hadoop framework is originally written in java then why companies are willing to hire candidates fluent in python? Let us find out the importance of python in Hadoop in this article.
It is possible to write the codes for the Hadoop framework in python and it is compatible with the Hadoop distributed file system. All the analysis applications can be performed with the Hadoop framework coded in python. Python is easy to learn and use and yet is powerful in performing big data applications.
It has a big library of in-built functions which can be used as and when required. Python is a predictive language that has less syntax and semantic constraints as compared to other languages.
A lot less time is wasted in coding in Python due to its predictive nature and that’s why companies and firms are looking for candidates fluent in python, individuals who can solve big data problems with the help of python in a more efficient way. Python has a lot of remarkable applications such as Instagram, Google, Quora, etc. Facebook uses python with HDFS for data extraction and its parallel processing.
The libraries of python fit right in the slot for big data analytics. It makes coding convenient and fast. Users choose among various python frameworks available in the market for working with Hadoop such as Hadoop streaming API, Dumbo. Pydoop, etc.
These frameworks help to enable Hadoop with the help of python and using its services. Real-time computation can be done through python. Python has lists, tuples, dictionaries, etc. as data structures. These data structures can be used for high-end evaluation of big data.
The codes written in python are scalable and scalability is one of the main features of big data. Python is used a lot nowadays for application and web development. Python has an in-built mechanism and algorithm to deal with unstructured data and for doing the processing of that unstructured data. For example, NumPy is an in-built function in the python library that supports complex operations and scientific computing.
There are many other functions that support data analytics. When used in Hadoop, python increases efficiency and fault tolerance. Python boasts a strong user base throughout the world, there is an active community of people working on python which will help you by giving their approach to any particular problem.
A lot of research material and learning guide can be found on python as it is a globally used language. Big data and its applications are also being used by firms to enhance their business and predict trends and solutions. For this Hadoop training is being used and if we are getting such scalable language with an advanced library and is also easy to use, we are bound to use it!
Conclusion
So, each day new languages are coming but that doesn’t mean you have to learn them all. If you are working on the Hadoop platform then python is by far the most suited language for it. You can code much faster in python as compared to other programming languages and also with the chance of getting fewer errors and warnings due to its interactive and predictive nature. Hadoop and python have shown a lot of compatibility in big data use cases across the globe by firms and companies. This article highlights the importance of python in Hadoop.
