This project demonstrates a strong understanding of full end-to-end data pipeline. Using Python (pandas), Azure Blob Storage, Azure SQL database, Azure Data Studio, Azure Co-Pilot, Azure AI Text Analytics and Snowflake, this project analyzes customer feedback and bins negative, neutral, positive feedback. This prcoess helps tech startups with insights into customer service rep performance.
Data Extraction & Cleaning:
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Structured customer feedback data is extracted from various sources.
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A Python script cleans and preprocesses this data.
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The cleaned data is uploaded to Azure Blob Storage and Azure SQL Server through Data Studio.
Sentiment Analysis:
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Azure AI Language service analyzes the sentiment of the feedback (positive, negative, neutral).
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Data Loading into Snowflake.
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The processed data is staged in Azure Blob Storage.
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Integrate Snowflake with Azure Blob Storage to load data into Snowflake
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A Snowflake view is created to summarize agent sentiment performance metrics.
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Azure Blob Storage for cloud data storage
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Azure SQL Server for relational databases
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Azure AI Language for sentiment analysis
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Snowflake for data warehousing and analytics
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Azure Data Studio for database/table/view creation and reporting
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Azure Co-Pilot for queries and project management
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Python (Pandas) for scripting and uploading to Azure Blob Storage
