In today’s data-driven world, the volume, variety, and velocity of data generated by businesses, consumers, and systems have grown exponentially. This phenomenon, commonly referred to as “Big Data,” represents data sets that are too large or complex to be processed using traditional data processing techniques. Big Data encompasses structured, semi-structured, and unstructured data, often requiring specialized tools for storage, processing, and analysis. Python has gained significant traction as a go-to language for handling Big Data due to its simplicity, versatility, and powerful libraries. While Python on its own may not be designed to handle massive data volumes like distributed systems (e.g., Hadoop or Spark), it provides a rich ecosystem for integrating with these Big Data platforms and performing data analysis, processing, and visualization.The objective of this project is to explore the use of Python in processing, analyzing, and visualizing Big Data. By integrating Python with Big Data tools such as Apache Spark and Dask, the project will demonstrate the ability to process and derive insights from large-scale datasets efficiently.