Serverless Data Analytics with Flint
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Serverless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. In this thesis, we explore a completely different part of the application space and examine the feasibility of analytical processing on big data using a serverless architecture. We present Flint, a prototype Spark execution engine that takes advantage of AWS Lambda to provide a pure pay-as-you-go cost model. With Flint, a developer uses PySpark exactly as before, but without needing a Spark cluster and only paying for the execution of individual Spark programs. We describe the design, implementation, and performance of Flint, along with the challenges associated with serverless analytics.
Cite this version of the work
Youngbin Kim (2018). Serverless Data Analytics with Flint. UWSpace. http://hdl.handle.net/10012/13681