I recently evaluated Dagster, Prefect, and Flyte for a data pipeliney workflow and ended up going with Temporal.
The shared feature between Temporal and those three is the workflow orchestration piece. All 3 can manage a dependency graph of jobs, handle retries, start from checkpoints, etc.
At a high level the big reason they’re different is Temporal is entirely focused on the orchestration piece, and the others are much more focused on the data piece, which comes out in a lot of the different features. Temporal has SDKs in most languages, and has a queuing system that allows you to run different workflows or even activities (tasks within a workflow) in different workers, manage concurrency, etc. You can write a parent workflow that orchestrates sub-workflows that could live in 5 other services. It’s just really composable and fits much more nicely into the critical path of your app.
Prefect is probably the closest of your list to temporal, in that it’s less opinionated than others about the workflows being “data oriented”, but it’s still only in python, and it deosn't have queueing. In short this means that your workflows are kinda supposed to run in one box running python somewhere. Temporal will let you define a 10 part workflow where two parts run on a python service running with a GPU, and the remaining parts are running in the same node.js process as your main server.
Dagster’s feature set is even more focused on data-workflows, as your workflows are meant to produce data “assets” which can be materialized/cached, etc.
They’re pretty much all designed for a data engineering team to manage many individual pipelines that are external from your application code, whereas temporal is designed to be a system that manages workflow complexity for code that (more often) runs in your application.