The Hadoop framework is written in java language but it is absolutely possible for Hadoop programs to be coded in Python or C++ Language. This implies that data architects don’t have to learn java if they are familiar with python. The python comes across as one of the most user-friendly to learn and adapt language and yet extremely powerful for end-to-end advanced analytics applications. It can be possible to write programs like map-reduce in python language without the need for translating the code into java jar files.
Python as a programming language is easy to understand and flexible. It is capable and powerful enough to run end-to-end advanced analytical applications.
Now, let’s take a look at how some of the top-notch global companies were using Hadoop in association with python:
1) Facebook :
In the domain of image processing, facebook is second to none. Each day the facebook processes millions and millions of images based on unorganized data for that facebook had to enable HDFS; it helps to store and extract tremendous volumes of data while using the Python as a backend language to perform a large chunk of its Image Processing Applications including Image resizing, Facial Image Extraction, etc.
While working on a big data project, the application developers have the option to choose from a myriad of programming languages– Python, Java, R, SQL, Julia, Scala, C, and MATLAB. But the latest usage statistics posted on multiple websites depict that a large percentage of application developers and data scientists prefer python to another programming language.
At present, Python is one of the widely used general-purpose programming languages. The software developers use Python for developing a variety of desktop GUI applications and web applications. Also, Python does not come with any native features to simplify big data application development. But Python, unlike other programming languages, emphasizes code readability.
2) Amazon :
Based on consumer research and buying patterns, Amazon recommends suitable products to the existing users. This is done by a robust machine learning engine powered by Python, which seamlessly interacts with the Hadoop ecosystem, aiding in delivering top of the line product recommendation system and boosting fault-tolerant database interactions.
3) Quora search algorithm :
Quora’s backend is constructed on Python; hence its the language used for interaction with HDFS. Also, Quora need to manage a vast number of textual data, thanks to Hadoop, Apache spark and a few other data-warehousing technologies. Quora uses the power of Hadoop coupled with python to drag out questions from searches or for suggestions.
Python Hadoop features and advantages :
a) Python with Apache Hadoop is used to store, process and analyze raw data sets. For unprocessed applications, we use python to write map-reduce programs to run on a Hadoop cluster. Hadoop has become a standard in distributed data processing but depends on java in the past. Today, there are many open-source projects that support Hadoop in Python. Python supports other Hadoop ecosystem projects and its components such as HBase, Hive, Spark, Storm, Flume, Accumulo, etc.
b) Hadoop requires Java runtime environment JRE 1.6 or higher because Hadoop is developed on top of java APIs. Hadoop works from low-level single node to a high-level multi-node cluster environment. Map-reduce is mainly used for the simultaneous processing of large sets of data. initially, it is a proposal designed by google to provide parallelism data distribution and fault-tolerance.
c) The reasons behind using Hadoop with python instead of java are not all that different than the classic java vs. python arguments. One of the most significant differences is that we don’t have to compile our code instead we can use a scripting language. This makes more interactional development of analytics possible makes maintaining and fixing applications in production environments compact and simpler to read code and so much more. Also by integrating python with Hadoop, you get access to world-class data analysis libraries such as numpy, scipy, nltk and scikit-learn that are object-oriented for both inside of Python and outside.
Great blog. It was a good read.