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Developed and executed an end-to-end data analysis pipeline for a retail store, leveraging AWS S3 for data storage and Snowflake for data warehousing. Utilized Python in Jupyter Notebook for data transformation and cleaning, ensuring high-quality data preparation for analysis.
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Established a robust data integration process, connecting AWS S3 buckets to Snowflake, enabling seamless data extraction and loading into Snowflake tables. Enhanced data accessibility and manipulation by creating a connection between Snowflake and a local Jupyter Notebook environment.
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Designed and implemented an interactive dashboard in Tableau, utilizing Snowpipe to stream data from Snowflake, providing real-time insights and visualizations of retail trends based on comprehensive datasets, including transactions, products, coupons, and demographics.
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Automated the data update process by setting up a cron job in Jupyter Lab, integrated with Amazon SQS, to trigger updates in the Tableau dashboard whenever new data is uploaded to the AWS S3 buckets, ensuring real-time data accuracy and relevance in visual analytics.
Technologies Used: Snowflake, AWS, Python, SQL, and Tableau.
