We aggregated data from diverse sources, obtaining information through various channels, including direct access to platforms and reception of a segment via email, specifically vendor-supplied data. For platforms with direct access, we leveraged an API to seamlessly integrate the data into our PostgreSQL database, serving as our designated data warehouse. Given that the data received via email was unprocessed, we implemented a Python code to scan and modify the information systematically, minimizing the need for manual intervention and reducing the overall time invested. Post-cleansing incorporated the refined data into our PostgreSQL database. To link our data to BigQuery, we employed Google Cloud services as an intermediary to transform the data originating from PostgreSQL. The data's journey, from its inception to its final repository in BigQuery, underscores the efficiency of our daily data pipeline. This automated process meticulously navigates through each stage of data transformation. The entire pipeline operates automatically on a server deployed remotely. This deployment guarantees the consistent and automatic execution of the job, facilitating daily data updates and streamlining data management in our remote environment.