When dealing with larger data sets like the one we’ll be working on, we have to consider how to process the data in a reasonable amount of time, and doing so in a way that would be reliable such that it doesn’t break in the middle. Fortunately, there are open source tools as well as cloud solutions that can help us achieve this:

Ray is an open-source framework that provides a simple, universal API for building distributed applications. It is designed to scale Python applications from a single machine to a large cluster with minimal effort. Ray is widely used for machine learning and other computationally intensive tasks, as it can efficiently distribute these tasks across a cluster of machines.

Anyscale offers a cloud service based on the Ray framework. It simplifies the process of deploying, scaling, and managing Ray applications in the cloud. Anyscale's platform enables users to seamlessly scale their Ray applications from a laptop to the cloud without needing to manage the underlying infrastructure, making distributed computing more accessible and efficient.