  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 paper, 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 an
actual Spark cluster. We describe the design, implementation, and performance
of Flint, along with the challenges associated with serverless analytics.
