Why Do We use python in hadoop
5 Reasons to Choose Python for Big Data
Analysis
● 1. Less is More:
● Python is known for making programs work in the least lines of code.
It automatically identifies and associates data types and follows an
indentation based nesting structure. Overall the language is easy to
use and takes less time in coding. There is also no limitation to the
data processing. You can compute data in commodity machines,
laptop, cloud, desktop, basically everywhere. Earlier Python was
argued to be slower than some of its counterparts like Java and Scala
but with Anaconda platform it has caught up to speed. Hence it is
fast in both development and execution.
2. Python’s Compatibility with Hadoop:
● Hadoop is the most popular open-source big data platform and the
inherent compatibility of Python is yet another reason to prefer it
over other languages. The PyDoop package offers access to the HDFS
API for Hadoop and hence allows to write Hadoop MapReduce
programs and applications. Using HDFS API you can connect your
program to an HDFS installation thus, making it possible to read, write
and get information on files, directories, and global file system
properties. PyDoop also offers MapReduce API for complex problem
solving with minimal programming efforts. This API can be used to
seamlessly apply advanced data science concepts like ‘Counters’ and
‘Record Readers’.
3. Ease of Learning:
● Compared to other languages Python is easy to learn even for nonprogrammers. It makes an ideal first language due to three primary
reasons — ample learning resources, readable code and large
community. All these translate to a gradual learning curve with direct
application of concepts in real-world programs. The large community
also means that in case you get stuck there will be many fellow
developers who will be happy to solve your issues.
4. Powerful Packages:
● Python has a powerful set of packages for a wide range of data
science and analytical needs. Some of the popular packages that give
this language an upper hand include:
● NumPy — used for scientific computing in Python. It is great for
operations relating to linear algebra, Fourier transforms, and random
number crunching. It works well as a multi-dimensional container of
generic data hence, can effortlessly integrate with many distinct
databases.
● Pandas — a Python data analysis library that offers a range of
functions for dealing with data structures and operations like
manipulating numerical tables and time series.
● Scipy — library for scientific and technical computing. SciPy contains
modules for common data science and engineering tasks like linear
algebra, interpolation, FFT, signal and image processing, ODE solvers.
● Scikit-learn — useful for classification, regression and clustering
algorithms like random forests, gradient boosting, k-means etc. It
inherently compliments other libraries like NumPy and SciPy.
● PyBrain — it is short for Python-Based Reinforcement Learning,
Artificial Intelligence, and Neural Network Library. PyBrain offers
simple yet still powerful algorithms for Machine Learning tasks along
with the ability to test and compare algorithms using a variety of
predefined environments.
● Tensorflow — a Machine Learning library developed by Google’s
team for research in deep neural networks. Its data flow graphs and
flexible architecture allow operations and computation of data, with a
single API, in multiple CPUs or GPUs in a desktop, server, or mobile
device.
5. Data Visualization:
● Though Python toughest competitor R is better when it comes to
data visualization, with recent packages Python has improved its
offering in this space. We now have many cool APIs like Plotly and
libraries like Matplotlib, ggplot, Pygal, NetworkX etc. that can create
breathtaking data visualizations. You can even use TabPy to integrate
Tableau and use win32com and Pythoncom to integrate Qlikview,
both are popular big data visualization tools.
