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Python, developed by Guido van Rossum in the late 1980s, is a general purpose, high-level programming language that emphasizes on code readability & simple syntax. Let us look at how Python gets along with Big Data!
Python for Big Data
Typically, Python’s simple syntax and gradual learning curve has been one of the most popular reasons as to why it’s used in Big Data. It would be interesting to know that interns in organizations are actively engaged in teaching the language to new employees. To get in-depth knowledge on Python along with its various applications, you can enroll for live Python online training with 24/7 support and lifetime access.
AppNexus, one of Python’s loyal users states, “We’ve been able to build a framework that makes it easy for us to grab data from all of these disparate data sources and model them. So instead of everyone spending their time writing database connector code, they are able to use a simple configuration and quickly get off the ground”
Subsequently, Python allows organizations to move code from development to production more quickly since the same code made as a prototype can be moved into production.
We all know that Hadoop is an important technology that has gained huge popularity as a Big Data Solution, but did you know that Python is used to write Hadoop’s MapReduce programs and applications to access HDFS API for Hadoop with PyDoop Packages?
Let us look at PyDoop, an application package that provides a Python API for Hadoop’s MapReduce and HDFS. Perhaps one of the most important link between Python and Big Data, the PyDoop has several advantages over Hadoop’s built-in solutions for Python programming which includes Hadoop Streaming.
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The biggest advantage of PyDoop is it’s HDFS API. This allows one to connect to an HDFS installation, read and write files , and get information on files, directories and global file system properties.
The MapReduce API of PyDoop allows one 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.
Python Trends today
As per the job trends on Indeed.com, Python and R combination with Big Data is picking up steadily. With many companies looking for Big Data analytics, python training seems to be a must on your resume. Python is by far the most in demand of the three, with some 27,000 jobs in the Big Data field (Source – Info world). Python for Big Data Training automatically qualifies you for those jobs.
Completing the Python training helps you in finding high-paying jobs within a short time. With many more jobs coming up in Big Data, Python training will make you the ideal candidate.
Despite its simplicity, Python is vastly powerful for solving complex and difficult data analytical problems in virtually any domain. Python is platform independent, and so it can integrate with most existing IT environments. Python has high capabilities for Big Data manipulative tasks and its natural strength as a scripting language makes it highly adaptive for data–oriented applications. No wonder, companies of all sizes and different industry types are using Python to manage their Big Data requirements. As companies continue to leverage the power of Python for Big Data processing, Python training will help establish your skills in Big Data analytics.
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