How Can Companies Leverage Python and Big Data to Drive Results?

Python and big data have gained immense popularity among all kinds of companies across industries. With data growing exponentially, Python has become one of the superior technologies to manage big data with the help of its extensive library support, statistical analysis capabilities, and more. According to Statista, annual revenue from the global big data analytics market is expected to reach over 68 billion U.S. dollars by 2025.

Tech giants such as Google, Facebook, Netflix, Instagram, etc., are using Python and big data to deliver exceptional user experience, boost productivity, improve efficiency, and make informed decisions. Companies are hiring Python developers with expertise in handling big data analytics to harness the power of data to achieve their business goals.

Applications of Python and Big Data

Businesses use data to analyze patterns and derive meaningful insights that help them make informed decisions. Large amounts of heterogeneous data can be combined and analyzed to solve critical business problems. Data engineering aids the process by allowing data analysts, data engineers, researchers, and managers, to evaluate data in a secure, trustworthy, rapid, and comprehensive manner. 

Let’s have a look at the applications of Python and big data:

  • Gathering of data: Python is used to obtain data from APIs or through web crawlers. Furthermore, Python abilities are required for scheduling and orchestrating ETL tasks using platforms like Airflow.
  • Manipulation of data: Python libraries such as Pandas, TensorFlow, NumPy, etc., make it possible to organize and analyze big data. Furthermore, Python for data engineering includes a PySpark interface for working with huge datasets in a distributed environment.
  • Data modeling: TensorFlow/Keras, Scikit-learn, and Pytorch are some popular Python frameworks for running machine learning or deep learning models. As a result, it creates a common language for successful communication between teams.
  • Surfacing of data: There are many data surface options, such as putting data into a dashboard or traditional report or just providing data as a service. With frameworks like Flask and Django, Python is essential for creating APIs to surface data or models.

Some of the crucial applications of big data also include behavioral analytics, customer segmentation, customer sentiment analysis, fraud detection, and more.

Benefits of using Python for Big Data

  • Open-source and easy to learn: Python is an open-source programming language with a community-driven development strategy. It’s free to use, and because it’s open-source, it works on various systems and environments (Linux, Windows, etc.). Python is also straightforward to learn because of its syntax. Instead of wasting time understanding the technical complexities of the language, big data specialists may focus on insights and managing data with this easy, accessible syntax. 
  • Python supports multiple libraries: Python is a well-known programming language due to its broad library support.  Most Python libraries can be used for data analytics, visualization, numerical computation, and machine learning. These libraries save time while also increasing the popularity of the language. Big data necessitates a lot of scientific computing and data analysis, and Python is a perfect choice to get it done.
  • High processing speed: Python’s great data processing speed makes it ideal for big data applications. Because of its simple syntax and easy-to-manage code, Python routines run at a fraction of the time, unlike other programming languages. It supports many prototyping approaches, allowing it to run code faster while preserving good code-to-execution transparency.
  • Data processing support: Python has an in-built feature that allows it to process unusual and unstructured data, which is the most prevalent requirement for data analysis. 
  • Large community support: Big data analysis is typically used to solve complex challenges that require community support. Python has a vast and active community that provides expert advice on coding challenges to data scientists and programmers.
  • Huge scope: Python is an object-oriented programming language that can handle complex data structures. Users can infer data structures such as lists, sets, tuples, dictionaries, etc. It also supports data frames, matrix operations, and other scientific computing activities. Hiring dedicated Python developers can help you expand the language’s breadth, allowing it to simplify and accelerate data operations.
  • Smart hiring choices with data: Staffing is a crucial decision-making aspect where firms can benefit from data. Due to personnel shortages and skills gaps, finding competent talent can be difficult, especially in high-skill areas like technology and healthcare. Data-driven insights can help with more than just the employment process. After an employee has been onboarded, data can be used to improve training approaches. 
  • Competitive advantage: Data and analytics are indisputably a part of the modern workplace, and the technology and tools available to put them to good use have made them more accessible.  The companies with the most advanced analytics skills tend to be ahead of their competitors. Not only are such firms twice as likely to be in the top quartile of their industry’s financial performance, but they’re also five times more likely to make choices faster than their competitors. Companies that do not mine their own value risk slipping behind their more data-driven, forward-thinking competitors.

Final words 

Big data technology is gaining traction around the world, and addressing industry demands is proving to be a challenging undertaking. Python, on the other hand, has become an excellent choice for big data due to its numerous advantages. To summarize, combining big data with Python provides a strong computing capability and allows companies to make well-informed decisions and drive exponential growth.