What
Makes Python the Best Programming Language to Write Big Data Apps?
Supports
Common Internet Protocols and Data Formats
Python
allows developers to create web services easily by invoking COM or
CORBA components. The programming language further allows programmers
to call components directly to and from the code written in Java, C
or C++. At the same time, Python also supports the common data
formats and internet protocols. In addition to providing powerful
process control capabilities, Python also processes XML and other
markup languages. The programmers also have option to create internet
application by embedding Python like a programming language. The
feature makes Python effective in supporting and processing a wide
variety of commonly used data formats.
Meets
the Requirements of Complex Multi-Protocol Network Applications
The
big data applications are required to simultaneously process a large
number of communication links. Along with database and network
communication, the application also needs to run a large number of
concurrent inter-process communications. Often a single process needs
to simultaneously process a large amount and wide variety of
communications. The Python programmers can use a software development
framework called Twisted to create complex multi-protocol network
applications that need to simultaneously process a large amount and
wide variety of communications.
Just-in-Time
(JIT) Compilers
Many
Python developers use JIT compilation to increase the programming
language’s speed and performance. Like Java Virtual Machine (JVM),
the JIT compilation detects only the specific bytecode that are under
execution as hotspots. For instance, the Python programmer can use
the alternative interpreter provided by PyPy to execute the Python
code without any alteration. The framework can effectively execute
the original Python code with average speedups of 6.3x. However, PyPy
cannot be used effectively for creating numerical applications as it
does not support Numpy arrays fully. So the developers have to
additionally use numexpr as a JIT compiler to evaluate all complex
numerical expressions at a rapid pace.
Libraries
to Analyze and Manipulate Data
The
Python libraries help big data applications in analyzing and
manipulating a huge amount of data. The latest applications also
require the data scientists and business analysts to use agile tools
to effectuate the data analysis and manipulation. As a powerful
language, Python can be used by these professionals to resolve
complex problems in any domain. Further, they have option to
integrate Python with their existing IT infrastructure. So the
Python-based big data applications can effectively analyze and
manipulate large amount of data in almost all domains.
However,
many businesses are still skeptical about the significance of Python
in the big data arena due to the poor performance of pure-Python
applications. But the users still have option to overcome the
disadvantage by linking Python with library extension written in
other programming languages like C and FORTRAN. Also, the business
can avail the benefits of certain open source projects to optimize
the speed and performance of Python by compiling it into native code.
The innovative projects further make Python the best programming
language to write mission critical and big data applications. You can
get in touch with a custom
python development company
who can help you develop web apps that are stable, scalable and
secure.
We
provide Python
development
services. If you would like hire
python developers
for your development needs, please contact us at Mindfire Solutions.
