Why should you use Python for Big Data?

Python for Big Data

While working on a big data project, the data scientists and 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.

It helps developers to accelerate big data application development by writing code that can be read and understood by humans easily. Also, Python simplifies maintenance of big data applications by keeping codebases concise. At the same time, Python is currently supported by a wide range of popular big data application development and processing frameworks. There are also a number of reasons why you must use Python while working on big data projects.

6 Reasons Why You Must Use Python while Working on Big Data Projects

1. Easy to Learn and Use

Python is considered as one the best programming language for beginner-level programmers. The interpreted programming language features a simple structure and clearly defined syntax rules. It further does not require programmers to remember a large number of keywords. Hence, beginners can learn and use Python without putting extra time and effort. Python enables developers to start building big data applications in a short amount of time.

2. Maintainable Codebase

In addition to featuring clearly defined syntax rules, Python also emphasizes code readability and reusability. Its expressive syntax rules enable programmers to express concepts by writing clean and concise code. The clearly defined codebase further makes it easier for programmers to maintain and update the big data application. You must opt for Python to build big data application with easy-to-maintain code.

3. A Wide Range of Libraries

Python scores over other programming languages for big data in the category of a standard library. While working on big data projects, data scientists have the option to choose from a set of handy Python libraries – NumPy, SciPy, Scikit-Learn, Theano, Pandas, Statsmodels and NLTK. You can take advantage of specific Python libraries to simplify big data application development efforts like applying mathematical operations on entire datasets, using high-level data structures, and implementing machine learning algorithms.

4. Many Data Visualization Tools

While working on big data projects, you need robust tools to detect trends, patterns, and correlation by analyzing large datasets visually. Python allows you to take advantage of a number of robust data visualization libraries/tools - Matplotlib, Seaborn, Bokeh and Altair. The data visualization libraries are designed with a built-in feature to simplify big data analysis by converting large datasets into an easy-to-comprehend visual format.

5. Interoperability

You can use specific libraries to integrate Python seamlessly with other widely used programming languages for big data. For instance, you can use Cython to reduce runtime by converting the Python code for the C environment. Likewise, PyMySQL helps you to extract data and run database queries from MySQL database by integrating it with Python. Also, Python is fully compatible with R. You have the option to call Python code directly from R code and run R code through Python. The interoperability will help you to build big data applications by availing language features of multiple programming languages.

6. Compatibility with Hadoop

Python is one of the programming languages which are fully compatible with widely used big data platforms like Hadoop. While writing MapReduce programs and applications in Python, you can use the PyDoop package to access HDFS API for Hadoop directly. The seamless access to the HDFS API will make it easier for you to read, write, and get information from various directories and files. You can even use the HDFS API to include the built-in solutions provided by Hadoop to Python programs without writing additional code.

On the whole, you have the option to choose from a number of programming languages while working on a big data project. You must opt for Python to accelerate big data application development and maintenance by writing clean, readable, and concise code. At the same time, you can take advantage of several robust libraries, frameworks, and development tools to build big data applications according to varied business needs.

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