Well, big data is creating ripples now. Having said that, every professional in the domain of big data struggles to choose the right programming language. In fact you have to choose the correct programming language in the beginning itself since it will be difficult to migrate once you begin the development. Some of the popular languages in this context are Python , R Programming, Java, SAS etc.
Several people say a lot of things with regard to the power of Python. But have you really thought about its usage in the context of big data. This is what this blog exactly deals with, Read further to understand the advantage of Python and why it is a popular language for big data.
“If you do the things that are easier first, then you can actually make a lot of progress”
Yes, Python is a very simple language and it will help you in making a lot of progress as you proceed. It places a lot of stress on readability and it is like everyday English. The learning curve is also short in Python. So data scientists prefer Python to a great extent.
Python is one of the most favored languages for big data. Though it has some limitations including lack of UI development framework, it is an ideal choice for big data due to the reasons mentioned above.
- Python essentially maintains its popularity due to its easy-to-use features that immensely supports big data processing. The powerful suite of libraries and utilities that Python offers for analytics and data processing activities places it in an advantageous position. These packages support large data science and analytical requirements eg, NumPy, Pandas, SciPy, MatPlotLib, etc. You can use the open source packages as per your requirement.
- Data scientists are generally involved in wiring together network applications,scripting, programming for the web, automating data processing jobs etc. Python is the best answer to perform all these tasks. This is mainly because of the strength of its core libraries, which we highlighted in the previous point.
- Data scientists spend a lot of time in cleaning unorganized data set. Pandas make this much easier. There is a concept called data munging here and in order to learn about it in detail you can enroll in SLA for Python Training . We teach you the essentials of Python and how it is useful to your career.
- When you are developing code there is more speed in Python for Big Data compared to any other programming language. Well, by now you would be more curious about the concept of Python. Call SLA @ +91 86087 00340 now for a free demo session.
- Hadoop is the most famous open-source big data platform. The innate compatibility of Python makes it a favorite for big data. Python is used to code MapReduce programs in Hadoop using the PyDoop package. PyDoop also provides MapReduce API for difficult problem solving with less programming skills.
- Python is reputed for making programs function in the least lines of codes. It automatically finds out and links data types. In order to know about these in depth, you can call SLA’s technical managers and get to know the importance of Python.
- Python integrates excellently with other open source platforms that are frequently used in Big Data (Spark, Hadoop etc.).
- Python is Open Source in the sense that it is in the public domain and can be used by anyone freely. Anyone can modify the program and create their own versions to perform particular tasks. This is also one of the primary reasons that the open source nature of Python has been excitedly adopted by Big data enthusiasts. There is lot of flexibility in Python.
- Scalability is of great concern when you are handling huge data. Python can be useful for developing most of the back end, data processing functions of stalwarts like Google. Facebook etc. In such companies the services are regularly seeing a raise in size and they need to be updated constantly. For such huge functionalities programmers should work in an environment where new code can be integrated in a jiffy without disturbance to users. Python is suitable for this purpose.
- Python is faster compared to other data science languages including R, Matlab etc. Yes, one cannot deny that there was complaint about its speed at the outset, but with Anaconda its speed performance has developed a lot.
Final note
We don’t deny that Python is a very simple language. But it is highly powerful for solving complex analytical problems in any field. Python is highly proficient for big data tasks and its advantage of being a scripting language (its each and every line adds a meaning though being created in less time) makes it flexible for data-focused applications.
These key points offer support for big data processing and produces rapid insights to the company. This swift and powerful insight is valuable to the companies. However, these insights change in a regular basis. In this regard, the companies need powerful languages to gain this value quickly. Python takes up a prominent role and supports the requirements of the business.