Case study introduction

Despite the vast agricultural land expansion and technical advancements around the world, crop yields will need to increase exponentially to meet the needs of our growing global population. Crop monitoring and yield estimation will be crucial to ensuring food security, especially as climate change continues to intensify and our natural resources are depleted. Crop yield prediction can be time-consuming and complex, so creating a GIS-enabled data pipeline can improve the efficiency of the prediction process.

Researchers working on food- and agriculture-related topics want readily available data that they can download and study. They require a resource that can be used to extract agricultural data from various sources, clean it, and use it to predict crop yields for countries around the world. This will save them time and money, and result in more informed and timely decisions regarding aid for the countries involved.

You will create the ETL workflow...