In this project, a substantial dataset of 70,000 rows was amassed from Otodom and Brightdata, demonstrating the ability to handle large-scale data collection.
The data warehousing was efficiently managed using Snowflake, a cloud-based platform known for its speed, flexibility, and ease of use.
The data transformation and translation were carried out using a combination of Python, Google Sheets, and the OpenStreetMap API. Python, with its powerful libraries and tools, was instrumental in processing and analyzing the data. Google Sheets provided a user-friendly interface for manual data manipulation, while the OpenStreetMap API was used to fetch geographical data.
The culmination of the project was the implementation of a comprehensive dashboard in Snowflake. This dashboard served as a dynamic and interactive tool for data visualization, enabling clear and concise representation of the processed data.
Looking ahead, this project has significant potential for expansion and further development. The methodologies and tools used here can be applied to a variety of other domains requiring large-scale data analysis and visualization. The use of Python and SQL allows for the integration of more advanced data analysis techniques, such as machine learning algorithms or predictive analytics. Furthermore, the dashboard can be enhanced with additional features, such as real-time data updates or more advanced interactive elements.
This project serves as a testament to the power of combining robust data collection, efficient data warehousing, and sophisticated data visualization tools. It underscores the potential of such an approach in deriving meaningful insights from large datasets, thereby aiding strategic decision-making processes.