Pros and Cons of Google Dataflow VS Cloud Run while pulling data from HTTP endpoint

This is a design approach question where we are trying to pick the best option between Apache Beam / Google Dataflow and Cloud Run to pull data from HTTP endpoints (source) and put them down the stream to Google BigQuery (sink).

Traditionally we have implemented similar functionalities using Google Dataflow where the sources are files in the Google Storage bucket or messages in Google PubSub, etc. In those cases, the data arrived in a 'push' fashion so it makes much more sense to use a streaming Dataflow job.

However, in the new requirement, since the data is fetched periodically from an HTTP endpoint, it sounds reasonable to use a Cloud Run spinning up on schedule.

So I want to gather pros and cons of going with either of these approaches, so that we can make a sensible design for this.