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Csound
SuperCollider
Sonic Pi
Pure Data
ChucK
Overtone
Klangmeister
Pyo
Amazon EMR
Google BigQuery
Google Cloud Dataflow
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Databricks
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Csound is a sound and music computing system which was originally developed by Barry Vercoe in 1985 at MIT Media Lab. Since the 90s, it has been developed by a group of core developers. A wider community of volunteers contribute examples, documentation, articles, and takes part in the Csound development with bug reports, feature requests and discussions with the core development team.
Although Csound has a strong tradition as a tool for composing electro-acoustic pieces, it is used by composers and musicians for any kind of music that can be made with the help of the computer. Csound has traditionally been used in a non-interactive score driven context, but nowadays it is mostly used in in a real-time context. Csound can run on a host of different platforms including all major operating systems as well as Android and iOS. Csound can also be called through other programming languages such as Python, Lua, C/C++, Java, etc.
One of the main principles in Csound development is to guarantee backwards compatibility. You can still render a Csound source file from 1986 on the latest Csound release, and you should be able to render a file written today with the latest Csound in 2036.
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Based on our record, Amazon EMR seems to be more popular. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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: about 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: almost 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
SuperCollider - A real time audio synthesis engine, and an object-oriented programming language specialised for...
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Sonic Pi - Sonic Pi is a new kind of instrument for a new generation of musicians. It is simple to learn, powerful enough for live performances and free to download.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Pure Data - Pd (aka Pure Data) is a real-time graphical programming environment for audio, video, and graphical...
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost