Agile Open Data Warehouse Lab: Kickstarting Practical Open Data Analytics
In the era of data-driven decision making, building a fast and flexible data foundation is essential. In this series, Agile Open Data Warehouse Lab, we combine public open data with agile data warehouse design to solve real-world challenges using Python and spreadsheets.
Background and Challenges
Traditional data warehouse development often involves large-scale, time-consuming projects that struggle to keep pace with change. Meanwhile, public open data has become increasingly rich and accessible. By leveraging these and starting small with an agile iterative approach, the goal is to build impactful analytical foundations for real business and social challenges in a short timeframe.
Features of This Case Study
- Developing iteratively in small functional units, growing practically with monthly themes
- Combining Python data processing with spreadsheet visualization
- Using cost-free open data for diverse analyses
- Enabling gradual skill development for eventual cloud migration
- Promoting two-way communication and community building via social media
Introduction of First Case Study Theme
Visualizing pollen allergy trends nationwide using open data, analyzing seasonal variations with Python and spreadsheets
Â
We introduce government pollen allergy statistics open data, then perform cleaning and processing in Python. Visualization through spreadsheets highlights peak periods and trend patterns by region.
Â
Â
Japanese site https://note.com/919jj/n/ne78cc310e36f
Â