"Birds in the Clouds": Adventures in Data Engineering

TL;DR

This paper describes how the Cornell Lab of Ornithology transitioned their bird migration data pipeline to the cloud, significantly reducing costs and enabling scalable, efficient data processing for scientific and educational use.

Contribution

The paper presents a practical approach for migrating complex data pipelines to the cloud using open source tools, achieving cost reduction and scalability.

Findings

Operating costs reduced by a factor of 6

Successful deployment of STEM maps on cloud infrastructure

Enhanced scalability of bird migration data processing

Abstract

Leveraging their eBird crowdsourcing project, the Cornell Lab of Ornithology generates sophisticated Spatio-Temporal Exploratory Model (STEM) maps of bird migrations. Such maps are highly relevant for both scientific and educational purposes, but creating them requires advanced modeling techniques that rely on long and potentially expensive computations. In this paper, we share our experience porting the eBird STEM data pipeline from a physical cluster to the cloud, providing a seamless deployment at a lower cost. Using open source tools and cloud "marketplaces", we managed to divide the operating costs by a factor of 6, making it possible to scale our pipeline on a research budget.