This project demonstrates a streamlined ELT data pipeline, showcasing a seamless flow of data from a web application to advanced analytics. The pipeline integrates multiple Google Cloud Platform (GCP) services: the WebApp collects and ingests data, which is stored in Google Cloud Storage (GCS). A Cloud Function triggers automated transformations, loading the processed data into BigQuery for scalable, efficient analysis. Finally, Looker Studio visualizes insights, enabling interactive reporting and data-driven decision-making. This architecture highlights the end-to-end data journey, leveraging cloud-native services for a robust, scalable, and efficient solution.

1. Global Retail Chain needs to consolidate and analyze its sales revenue
2. The company needs a web application to load the sales data kept in csv from all the geogaphy to a single location
3. From the single location the company wants to have analytics performed by it's Business Analyst

1. A simple Web application to upload the csv files of sales data
2. The Web Application should store these files in GCS Bucket
3. When the file is uploaded to GCS bucket one GCP Cloud function event must be triggered
4. The uploaded file in GCS should be loaded to Biggquery with help cloud run function
5. Required transformation is performed in BigQuery to get the sales revenue values
6. The transformed data must be saved in BigQuery View
7. Using Looker Studio the Dashboardcan be created for the bigquery data