Hey Guys, The big data and machine learning world is dominated by Python, Scala an R.
I'm a Swifter by heart, but not so much by tools of trait. I'd appreciate a constructive discussion on how that could be changed. While R is a non goal for obvious reasons, i'd argue that since both Scala and Python are general purpose languages, taking them head to head might be a low hanging fruit. To make the claim I'd like to reference to projects such as - Hadoop, Spark, Hive are all huge eco-systems which are entirely JVM based. - Apache Parquet, a highly efficient column based storage format for big data analytics which was implemented in Java, and C++. - Apache Arrow, a physical memory spec that big data systems can use to allow zero transformations on data transferred between systems. Which (for obvious reasons) focused on JVM, to C interoperability. Python's Buffer Protocol which ensures it's predominance (for the time being) as a prime candidate for data science related projects https://jeffknupp.com/blog/2017/09/15/python-is-the- fastest-growing-programming-language-due-to-a-feature-youve-never-heard-of/ While Swift's Memory Ownership manifesto touches similar turf discussing copy on write and optimizing memory access overhead it IMHO takes a system level perspective targeting projects such as kernel code. I'd suggest that viewing the problem from an efficient CPU/GPU data crunching machine perspective might shade a different light on the requirements and use cases. I'd be happy to learn more, and have a constructive discussion on the subject. Thank you, Max. -- puıɯ ʎɯ ɯoɹɟ ʇuǝs
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