  Function-as-a-Service (FaaS) is a type of serverless computing that allows
developers to write and deploy code as individual functions, which can be
triggered by specific events or requests. FaaS platforms automatically manage
the underlying infrastructure, scaling it up or down as needed, being highly
scalable, cost-effective and offering a high level of abstraction. Prototypes
being developed within the SKA Regional Center Network (SRCNet) are exploring
models for data distribution, software delivery and distributed computing with
the goal of moving and executing computation to where the data is. Since SKA
will be the largest data producer on the planet, it will be necessary to
distribute this massive volume of data to the SRCNet nodes that will serve as a
hub for computing and analysis operations on the closest data. Within this
context, in this work we want to validate the feasibility of designing and
deploying functions and applications commonly used in radio interferometry
workflows within a FaaS platform to demonstrate the value of this computing
model as an alternative to explore for data processing in the distributed nodes
of the SRCNet. We have analyzed several FaaS platforms and successfully
deployed one of them, where we have imported several functions using two
different methods: microfunctions from the CASA framework, which are written in
Python code, and highly specific native applications like wsclean. Therefore,
we have designed a simple catalogue that can be easily scaled to provide all
the key features of FaaS in highly distributed environments using
orchestrators, as well as having the ability to integrate them with workflows
or APIs. This paper contributes to the ongoing discussion of the potential of
FaaS models for scientific data processing, particularly in the context of
large-scale, distributed projects such as SKA.
