The project involved designing and implementing an end-to-end automated data pipeline for Thameslink UK to track and analyze customer-reported feedback on railway service failures and delays. The pipeline was designed to provide key insights into customer needs and identify areas for service improvement.
Azure Serverless Functions were used to ingest data from Twitter and apply sentiment analysis models to extract key insights. The results were stored in an Azure Serverless SQL database and designed interactive dashboards on Power BI for effective visualization and reporting.
As a part of the team, I developed the end-to-end data pipeline, which leveraged a scheduled Azure Serverless Functions for data ingestion, sentiment analysis models for data processing, and Azure Serverless SQL database for data storage. The results were visualized on Power BI dashboards for easy access and analysis.
Overall, I this was my first project where I built and end to end data pipeline which was deployed to the cloud.