Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

Businesses “Going Green” is so commonplace now it’s more than just an encouraging a trend; it’s become expected of companies big and small. In fact, consumers frequently place more of an obligation on companies to go green than they do themselves. The last few years—the infancy of what may turn out to be a green revolution—have really proven that there are many ways to go green, and that some of these ways are much more financially sound than others. But rather than providing a cut and dry list of green options and their pros and cons, I’d like to take a look at the bigger picture incentives for businesses going green and how consumers are responding.

What does it mean to be green?

If you are a software developer looking for a slight change, then you have several options available. The process of software development requires multiple types of resources. A software developer performs the construction and delivery of software programs. An experienced software developer gains business knowledge, analytical skills, team management skills and communication skills. All of these skills can be used to divert your development career into a related and slightly varied role in software development.

Production Support Engineer
A developer can easily switch to the role of a Production Support Engineer. This role entails working with customers and technical teams to report, track and resolve production issues. For some, this might be an exciting opportunity to see the software application from a user’s point of view.

Engineering Manager
If you have experience in leading a team of developers, you could take the role of an Engineering Manager. This role requires managing a bigger team of developers. The Engineering Manager is also responsible for ensuring the delivery of software products and meeting the deadlines set by Product Management. You will get the opportunity to develop software, if you are inclined to do so. However, you will also take new responsibilities such as performance management, infrastructure management and vendor management.

Partner Engineer
This role requires some amount development as well as coordination with partners such as vendors and customers. The job of a Partner Engineer is to act as a middleman to help the integration of services with partners via application programming interfaces (APIs). For example, companies such as Twitter and Facebook employ Partner Engineers to integrate their services with customer websites.

Systems Analyst
Many companies offer developers with an opportunity to switch to Analyst roles. This role involves analyzing system requirements by working with business and technical teams. Many Systems Analysts also work on reviewing, developing and testing application code. This role is suitable for developers with strong analytical skills.

QA Automation Engineer
This role is responsible for automating test cases with the help of tools such as Java, Ruby and Selenium. This role is ideal for people with prior development experience. QA Automation Engineers work with developers and product managers to define test cases, and to automate and run the test cases. In this role, you will get the opportunity to work on back-end as well as front-end automation tasks. You will remain in touch with programming languages as well as database technologies.

Database Analyst
Most people gain significant amount of knowledge on databases while working as a software developer. This will help you to switch your role into a Database Analyst. A Database Analyst analyzes database issues, reviews performance problems, writes database scripts and runs queries. This role also provides a path to become a Database Administrator, if you are interested.

Deployment Engineer
This role is responsible for deploying the code developed by software engineers. You may not be developing application programs in this role. However, you will be responsible for code deployments, pushing the code into test and production environments.

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Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

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