Check out ChucK also (https://chuck.cs.princeton.edu/). It's a very capable language and we'll documented. Source: about 1 year ago

I am a programmer by trade but don't often combine it with my musical endeavors. I briefly messed with https://chuck.cs.princeton.edu/ for live coding shows in college but honestly its very restrictive. Source: over 1 year ago

Also, a programming language geared towards music can help with process-driven composition. Max/MSP or ChucK for instance. Source: about 2 years ago

I haven't coded music in haskell, but I've coded it in Max/MSP and ChucK and I enjoyed them both https://chuck.cs.princeton.edu/ https://cycling74.com/products/max. - Source: Hacker News / over 2 years ago

ChucK: Strongly-Timed, Concurrent, and On-the-Fly Music Programming Language\ (15 comments). Source: over 2 years ago

I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / 13 days ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 27 days ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 2 months ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago

Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 6 months ago