FaaS and Furious: abstractions and differential caching for efficient data pre-processing

Abstract

Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to interact declaratively with assets in object storage. Motivated by real-world industry usage patterns, we exploit these new abstractions with a columnar and differential cache to maximize iteration speed for data scientists, who spent most of their time in pre-processing - adding or removing features, restricting or relaxing time windows, wrangling current or older datasets. We show how the new cache works transparently across programming languages, schemas and time windows, and provide preliminary evidence on its efficiency on standard data workloads.