Serverless Data Analytics (Finally) Bridging the Gap: Introducing the Ortzi DataFrame

Serverless technologies have simplified distributed computing by streamlining resource management and offering out-of-the-box usability of cloud resources. However, serverless computing has yet to fully permeate the broader data analytics community. One of the major reasons causing this slow adoption is the lack of a handy serverless interface for seamlessly running recurrent workloads in the cloud. To fill this gap, we introduce in this work the missing piece in serverless analytics: the Ortzi Dataframe, a practical and intuitive programming abstraction that mirrors pandas DataFrames, so that users can effortlessly run their local, single-threaded Python code at scale in the cloud. Needless to say, such a powerful abstraction is certainly useless if not backed by a serverless analytics system that can operate over it in parallel. For this reason, another major contribution of this paper is a fully-fledged system that can run jobs in parallel across the cloud continuum using the novel Ortzi Dataframes. The new system leverages the specific capabilities of each serverless backend without user intervention. Our evaluation demonstrates that Ortzi enables exploration of the nuanced trade-offs of heterogeneous backends with min-imal programming changes and overhead. By harnessing the seamless nature of Ortzi, we optimize jobs through strategic backend selection, still delivering a user-friendly open source framework for programmers without cloud expertise.