A Symphony of Data

  Introduction Imagine having the power to unveil hidden patterns in fintech data with just a few clicks. In the ever-evolving world of digital payments, understanding transaction trends and regional metrics can unlock transformative insights. That's exactly what I set out to achieve with my PhonePe Pulse Data Visualization project. By harnessing the capabilities of Python, MySQL, Streamlit, and Plotly, I created an interactive tool that not only simplifies data exploration but also reveals intriguing patterns in the PhonePe Pulse data. Join me as I walk you through this project that turns raw data into actionable insights and visually engaging stories.

Introduction  Ever wondered how much data YouTube holds beyond just your favorite videos? Imagine building an app that lets you peek behind the curtain and unlock valuable insights hidden in every channel. Dive in with us as we turn raw YouTube data into meaningful stories! In the age of digital media, data from platforms like YouTube can offer a wealth of insights, from understanding viewer preferences to analyzing engagement metrics. To tap into this potential, I embarked on a project to create a Streamlit application that allows users to harvest, store, and analyze YouTube channel data. This project involves several exciting technologies, including Python , MongoDB , SQL , Streamlit , and the YouTube API . The aim was to build a user-friendly application where data from multiple YouTube channels could be easily accessed, stored in a MongoDB database as a data lake and then migrated to a SQL database for further analysis.