Not really, but you can try it out on https://hopsworks.ai

It's conceptually similar to EMR in the way it works. You connect your AWS account and we'll deploy a cluster there. HopsFS will run on top a S3 bucket in your organization. You get a fully featured Spark environment (With metrics and logging included - no need for cloudwatch). UI with Jupyter notebooks, the Hopsworks feature store and ML capabilities that EMR does not provide.

Yeah, https://hopsworks.ai is an alternative to EMR that includes HopsFS out-of-the-box. You get $4k free credits right now - it includes Spark, Flink, Python, Hive, Feature Store, GPUs (tensorflow), Jupyter, notebooks as jobs, AirFlow. And a UI for development and operations. So kind of like Databricks with more of a ML focus, but still supporting Spark.

You could but I would do thorough real-world benchmarks.

EMRFS has dozens of optimisations for Spark/Hadoop workloads e.g. S3 select, partitioning pruning, optimised committers etc and since EMR is a core product it is continually being improved. Using HopsFS would negate all of that.

JuiceFS can used in AWS EMR cluster, we have customers using JuiceFS to address the consistency and performance issues from S3.

There is a blog post [1] talking about this use case. Unfortunately, we have not publish a English version, you can read it using google translate .

[1] https://juicefs.com/blog/cn/posts/globalegrow-big-data-platf...

Disclaimer: Founder of JuiceFS here.