Big Data-ing with Python

By StepUpwards Team, 3rd January 2022

Big Data, Data Science, Data Analytics, Machine Learning, Artificial Intelligence – aha the all now familiar terms, rather the ABC of today’s corporate language. Big Data is a highly valued resource in the business ecosystem today. Data science is a composite field that covers a lot really – from data collection, data cleaning, standardization, data analytics, visualization, and reporting - to turn raw data into actionable insights.

Some of the trending applications used in many industries are based upon the concepts of Data Science - be it gaming, healthcare, speech recognition, language processing, web crawling, airline route planning, targeted advertising, fraud & risk detection, user recommendations, advanced image recognition, or augmented reality.

The quantum of data being generated by companies is uber huge. According to the International Data Corporation (IDC), the worldwide data will reach 175 zettabytes by 2025. Now wrap your head around this - a zettabyte is the equivalent of a trillion gigabytes. Take that 175 times. That’s how fast data is exploding.

The field of data science didn’t just appear overnight. It has taken years to evolve, and today, thanks to modern computing, we can analyze and predict outcomes in minutes, which would have taken several manual human hours earlier.

Although the choice of programming language for managing big data may vary depending on projects and goals, Python stands out as the preferred programming language due to its easy readability, fewer coding requirements, statistical analysis capacity and tremendous library and community support.

This clearly demonstrates the popularity of Python over other programming languages. (Source: Technobrains)

Let's delve deeper into the subject and explore why Python complements big data and its astounding growth.

Free and Open Source

Python is an open-source programming language developed on a community-backed and support-based model. It's free to use, supports multiple platforms, and can run on any environment like Linux or Windows.

Easy to learn

Python uses simple syntax and hence is relatively easy to learn. Its simple, readable syntax helps analysts focus on managing data to create actionable and meaningful insights, rather than spending time and effort in understanding the technicalities of a language. This is one of the dominant reasons that experts choose Python.

Simple Coding

Python programming involves simple coding as compared to other programming languages. One can easily script and execute programs with a little coding.  Moreover, Python allows you to identify data types quickly and can process tasks within a short time.

Portable and Extensible

Python's portability and extensibility attributes are what make it extremely popular in data science. This feature allows easy cross-language operations. Its graphics processing models for Machine Learning make it the selected language amongst data science professionals.

Supports Multiple Libraries

Python supports many libraries, which are time-saving. Most of the Python libraries are superbly matched to machine learning, data analytics, and visualization. Let's look at some of the popular Python libraries and frameworks in the field of data science: 

Tensorflow, Keras – for Machine and Deep Learning

Scikit-Learn – for machine learning linked to regression, classification, and clustering.

Matplotlib, Seaborn – for Data Visualization

Numpy – to compute in arrays and multidimensional matrices. Numpy offers advanced-level mathematical functions to operate data with random number crunchings, linear algebra, etc.

SciPy – ideal library for scientific computing and technical computing on data. Permits data integration, interpolation, optimization, and modification.

Pandas – offers multiple data structures to handle data. Pandas also has tool support for reading and writing data between different data formats and in-memory data structures.

Hadoop Compatible

Both Python and Hadoop being open-source big data platforms makes them more compatible vis-à-vis other programming languages. The Python PyDoop Package offers excellent support for Hadoop. Some of the benefits of using Pydoop are:

Access to the HDFS API – which allows you to read and write information swiftly on directories and files seamlessly.

Offers MapReduce API – offers MapReduce API, enabling programmers to resolve complex problems with minimal effort. This API allows you to deploy advanced data science concepts like ‘Record Readers’ and ‘Counters,’ making Python the apt choice for Big Data.

Scalability and Speed

Python is scalable, which has a big impact when working with data. If the volume of data increases, Python easily manages it and increases the speed of processing data in direct proportions, unlike Java or R.

The Big Data and Python duo offers a dynamic, versatile, and highly advanced computational capability in big data analytics.

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