# If tuplex can do it. So can Julia!

**URL:** <https://discourse.julialang.org/t/if-tuplex-can-do-it-so-can-julia/64979>\
**Category:** Data\
**Created:** [July 20, 2021, 12:44pm UTC](https://discourse.julialang.org/t/if-tuplex-can-do-it-so-can-julia/64979 "2021-07-20T12:44:14Z")\
**Posts on this page:** 1\
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**Author:** ![dfdx](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dfdx/32/120_2.png) [@dfdx](https://discourse.julialang.org/u/dfdx)\
**Post date:** [July 20, 2021, 7:26pm UTC](https://discourse.julialang.org/t/if-tuplex-can-do-it-so-can-julia/64979/2 "2021-07-20T19:26:10Z")

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I think the question is not how to implement it, but who actually needs it. Some time ago I asked here [what people need from a distributed computation framework](https://discourse.julialang.org/t/what-do-you-need-from-a-distributed-computation-framework/55676) and got silence in response. Spark evaluated into just a more flexible SQL database. Distributed machine learning is mostly concerned with multi-GPU training and has its own frameworks. UDFs are rare and usually it’s easier to just implement them in the native language for a framework (e.g. in Java/Scala for Spark).

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