I assume you are talking about this https://dotnet.microsoft.com/en-us/apps/data/spark. Source: almost 2 years ago

Good question! The API and the authoring experience is .NET, but the backend is Apache Spark which is built on the JVM. We use the .NET for Apache Spark to do the parallization. Source: almost 2 years ago

Yes that's correct. SynapseML builds on top of the Apache Spark for .NET project which provides .NET support for the Apache Spark distributed computing framework. Apache Spark is written in Scala (a language on the JVM) but has language bindings in Python, R, .NET and other languages. This release adds full .NET language support for all of the models and learners in the SynapseML library so you can author... Source: almost 2 years ago

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: over 1 year ago

I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago

This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 2 years ago

Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago

Check out https://aws.amazon.com/emr/. Source: about 2 years ago