Python and Big Data: How to Master this Powerful Combination
Big Data is a combination of structured, unstructured, and semi-structured data collected by businesses and organizations that can be mined and used for gathering information, powering machine learning and AI-related projects, and carrying our predictive modeling, among other advanced analytics applications. Thus, it is safe to say that Big Data is the most valuable commodity of the digital world. With an incredible 2.5 quintillion bytes of data generated every day, 90% of the world data has been produced in the last two years alone. With data growing extensively, the Python programming language has been recognized as an ideal tool to manage Big Data. Python Big Data combination can prove to be fruitful for data scientists and analysts looking to harness the power of data to achieve business success.
Billions of users connect to social networks daily, share information, upload videos, images, and perform other activities. There is an overabundance of data. But not all this data is valuable. Python, with its capabilities for statistical analysis, easy readability, and robust library support for data science, can contribute to the easy management of Big Data.
If you are a data scientist or work in a similar field, we will discuss a few reasons why Python for Big Data is given so much prominence by data professionals and will emphasize how one can master this powerful combination.
Let’s jump right into it.
Why Choose Python for Big Data?
It is said that by the end of 2025, global data will reach 175 zettabytes. With this growth rate, it is becoming more and more difficult to manage and process Big Data. In the last few years, Python has emerged as the most preferred programming language for the majority of IT projects, and its easy integration with Big Data is what makes it an ideal choice for managing loads of data.
There are plenty of reasons why Python and Big Data go hand in hand. Let’s explore why.
#1 Python is Open-Source and Easy to Learn
First things first, Python is an open-source programming language that is free to use and is developed with a community-based model. Because of this nature, developers find it easy to integrate with multiple platforms. The best part is that Python can run on any OS or environment, including Windows, Linux, Mac, etc. Python helps data experts with its simple, readable syntax, allowing them to focus on insights instead of understanding the platform.
#2 High Compatibility with Hadoop
Data experts know the importance of Hadoop in Big Data management. The best part is that Python is more compatible with Hadoop as compared to other programming languages. Because of its extensive support for libraries, Python can be easily merged with Hadoop. There is also a PyDoop Package specifically designed for Big Data management that allows developers to solve complex problems with minimal efforts using MapReduce API. Moreover, with its HDFS API, developers can easily read and write information on files and directories without any hassle.
#3 Supports Multiple Libraries
As said earlier, Python offers extensive support for libraries that not only saves time but ensures aspects like machine learning, numerical computation, data visualization, and data analytics are easily carried out. Big Data with Python makes scientific computing and data analysis much more convenient. When we talk about Python libraries specific to Big Data management, SciPy, Scikit-learn, Numpy, and Pandas, among others, are among the most prominent names. Pandas, for example, is a free software library that enables developers to analyze and handle data. Numpy, on the other hand, makes scientific computing easy with arrays and multidimensional metrics. Scikit-learn makes machine learning-related tasks like clustering, classification, and regression, a lot easier.
#4 Data Processing Support
One of the primary reasons to use Big Data and Python together is because of the language’s support for data processing of unstructured and unconventional data. When it comes to analyzing Big Data, especially social media data, filtering and processing unstructured data is the key to retrieving valuable insights. Python makes it easy for data experts to process unconventional data and retrieve meaningful information from it.
#5 Portability and Scalability
Data experts perform many cross-platform operations for their machine learning models. Thus, they are on the constant lookout for portable programming languages that can ease the process, like using GPUs for machine learning, etc. Python solves this dilemma due to its extensible nature. Also, because it is fast, it solves the scalability issue as well. As the data volume increases, Python can increase data processing speed, making things easier for data scientists.
These factors make Python and Big Data a perfect fit for each other in the long run.
What Knowledge, Operations in Python are Needed to Do Big Data Operations?
Python is easy for beginners yet highly advanced for those who may need to solve more complex tasks. One can write a Python program in one simple line but it doesn’t mean that you can process data with ease. It requires the right knowledge and operational skills to deal with such a large amount of data.
- Learn how to code using Python
When it comes to using Python and Big Data, learning to code is essential. You need to code in order to conduct statistical and numerical analysis with massive data sets. Hands-on experience with programming will enable you to think like a programmer, which will make you a good Big Data scientist. Most importantly, it is vital to interact with databases via statements and queries. Databases and Big Data tools should be a part of your arsenal. Tools like HIVE, Scala, SQL, R, etc., are something that you should get comfortable with.
- Master the Python Shell
Python Shell command is highly important to understand. Python shell enables you to do transformations, open data sets, and run algorithms with just a single command line. Without proper knowledge of Python Shell, you will be packaging your program and submitting it to Apache Spark using spark-submit. Also, you should understand the primary disadvantage of spark-submit when processing Big Data. It doesn’t allow you to inspect variables in real-time. To tackle this issue, experienced Python programmers include values in a log. However, Python Shell solves this dilemma because when you use shell, that log/text is an object, and you can further work with it.
- Quantitative Skills
Apart from programming, Python Big Data analysis requires quantitative skills. Simply put, programming will help you do what you need to. However, the question is, what are you supposed to do? Having quantitative skills helps answering this question. For beginners, you should have a good grasp of matrix algebra, linear algebra, and calculus. Above all, statistics and probability are the two most critical quantitative skills to learn.
It is okay if some concepts are not clear. You can always look for a Python Big Data tutorial on the internet or take up a course to further your understanding of the technology.
How to Manage Big Data using Python?
When it comes to managing and processing large datasets, data experts often use MapReduce that works with Big Data, allowing a data scientist to map the data using specific attributes and then reduce the data using aggregation or transformation methods.
MapReduce works by, first of all, scanning a dataset based on its attributes. For example, a dataset contains information about dogs. MapReduce will map this information based on the breed and then reduce by summing those groups. As a result, you get a list of all the dog breeds and the sum of dogs in each of these breed groupings.
There are several libraries that you can use for data management and processing with MapReduce. Python helps in extracting the results you get for further processing, reporting, and visualization.
So, that’s it. This is our small Python Big Data tutorial, explaining how these two can be used combined to make things a lot easier. Big Data and Python go hand in hand, and as a data scientist, it is important to understand its true power. When used effectively, Python can make things easier for you – from processing data to filtering useful information, managing unstructured data, and more. But, it requires knowing certain Python skills and understanding their proper implementation.
Nevertheless, it is the future of data science. If you liked this post and found it informational, please share it with your peers. Subscribe to our email newsletter for more informational blogs like this.