  Data lakes hold a growing amount of cold data that is infrequently accessed,
yet require interactive response times. Serverless functions are seen as a way
to address this use case since they offer an appealing alternative to
maintaining (and paying for) a fixed infrastructure. Recent research has
analyzed the potential of serverless for data processing. In this paper, we
expand on such work by looking into the question of serverless resource
allocation to data processing tasks (number and size of the functions). We
formulate a general model to roughly estimate completion time and financial
cost, which we apply to augment an existing serverless data processing system
with an advisory tool that automatically identifies configurations striking a
good balance -- which we define as being close to the "knee" of their Pareto
frontier. The model takes into account key aspects of serverless: start-up,
computation, network transfers, and overhead as a function of the input sizes
and intermediate result exchanges. Using (micro)benchmarks and parts of TPC-H,
we show that this advisor is capable of pinpointing configurations desirable to
the user. Moreover, we identify and discuss several aspects of data processing
on serverless affecting efficiency. By using an automated tool to configure the
resources, the barrier to using serverless for data processing is lowered and
the narrow window where it is cost effective can be expanded by using a more
optimal allocation instead of having to over-provision the design.

Databases