Why You Should Choose Python For Big Data?
Python provides a huge number of libraries to work on Big Data. You can also work — in terms of developing code — using Python for Big Data much faster than any other programming language. These two aspects are enabling developers worldwide to embrace Python as the language of choice for Big Data projects.
It is extremely easy to handle any data type in python. Let us establish this with a simple example. You can see from the snapshot below that the data type of ‘a’ is a string and the datatype of ‘b’ is an integer. The good news is that you need not worry about handling the data type. Python has already taken care of it.
Now the million-dollar question is; Python with Big Data or Java with Big Data?
I would prefer Python any day, with big data, because in java if you write 200 lines of code, I can do the same thing in just 20 lines of code with Python. Some developers say that the performance of Java is better than Python, but I have observed that when you are working with a huge amount of data (in GBs, TBs, and more), the performance is almost the same, while the development time is lesser when working with Python on Big Data.
The best thing about Python is that there is no limitation to data. You can process data even with a simple machine such as commodity hardware, your laptop, desktop, and others.
Python can be used to write Hadoop MapReduce programs and applications to access HDFS API for Hadoop using the PyDoop package
One of the biggest advantages of PyDoop is the HDFS API. This allows you to connect to an HDFS installation, read and write files, and get information on files, directories, and global file system properties seamlessly.
The MapReduce API of PyDoop allows you to solve many complex problems with minimal programming efforts. Advance MapReduce concepts such as ‘Counters’ and ‘Record Readers’ can be implemented in Python using PyDoop.
In the example below, I will run a simple MapReduce word-count program written in Python which counts the frequency of occurrence of a word in the input file. So we have two files below — ‘mapper.py’ and ‘reducer.py’, both written in python.
This is a very basic example, but when you are writing a complex MapReduce program, Python will reduce the number of lines of code by 10 times as compared to the same MapReduce program written in Java.
Why Python makes sense for Data Scientists
The day-to-day tasks of a data scientist involve many interrelated but different activities such as accessing and manipulating data, computing statistics and creating visual reports around that data. The tasks also include building predictive and explanatory models, evaluating these models on additional data, integrating models into production systems, among others. Python has a diverse range of open source libraries for just about everything that a Data Scientist does on an average day.
SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. There are many other libraries that can be used.
The verdict is, Python is the best choice to use with Big Data.
If you wish to check out more articles on the market’s most trending technologies like Artificial Intelligence, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series that will explain the various other aspects of Python and Data Science.
1. Machine Learning Classifier in Python
2. Python Scikit-Learn Cheat Sheet
4. Python Libraries For Data Science And Machine Learning
9. OOPs Interview Questions and Answers
10. Resume For A Python Developer
11. Exploratory Data Analysis In Python
12. Snake Game With Python’s Turtle Module
14. Principal Component Analysis
15. Python vs C++
16. Scrapy Tutorial
17. Python SciPy
18. Least Squares Regression Method
19. Jupyter Notebook Cheat Sheet
20. Python Basics
23. Python Decorator
25. Mobile Applications Using Kivy In Python
26. Top 10 Best Books To Learn & Practice Python
27. Robot Framework With Python
28. Snake Game in Python using PyGame
29. Django Interview Questions and Answers
30. Top 10 Python Applications
31. Hash Tables and Hashmaps in Python
32. Python 3.8
34. Python Tutorial
Originally published at https://www.edureka.co on December 3, 2015.
