In response to the imperative for data-driven decision-making in the stock market, this project presents an Automated Extract, Transform, Load (ETL) Pipeline tailored to streamline stock market data acquisition and analysis. Utilizing AWS infrastructure and Deepnote for robust data handling, the pipeline extracts raw data from diverse sources, applies essential transformations and loads the refined data into an AWS database. Scheduled execution and monitoring ensure real-time updates, while Data Studio enables dynamic visualization for insightful analysis. The project’s focus on maintenance and optimization ensures sustained peak performance. Additionally, the integration of machine learning algorithms automates technical analysis, empowering users to make informed buy/sell decisions based on data-driven insights, thus bridging the gap between traditional investment strategies and advanced analytics.