Hi Gianluna, Does the Python client miss any functionality or performance compared to Java?
Thanks On Fri, 5 Jan 2024 at 15:55, Gianluca Bonetti <gianluca.bone...@gmail.com> wrote: > Hello Angelo > > It seems to be an interesting use case for Ignite. > > However, you should consider what Ignite is, and what is not. > Essentially, Ignite is a distributed in-memory database/cache/grid/etc... > It also has some distributed computing API capabilities. > > You can store data easily in Ignite, and consume data by your code written > in Java. > You can also use Python since there is a Python Ignite Client available if > it makes your time series analysis easier. > You can also use the Ignite Computing API to execute code on your cluster > https://ignite.apache.org/docs/latest/distributed-computing/distributed-computing > but in this case I think Python is not supported. > > Cheers > Gianluca Bonetti > > On Fri, 5 Jan 2024 at 08:52, Angelo Immediata <angelo...@gmail.com> wrote: > >> I'm pretty new to Apache Ignite >> >> >> I asked this also on stackoverflow ( >> https://stackoverflow.com/questions/77667648/apache-ignite-time-series-forecasting) >> but I received no answer >> >> I need to make some forecasting analysis >> >> Basically I can collect data in Ignite in real time. Ignite will store >> data in its own caches >> >> Now I need to make some forecasting showing me the distribution of data >> in the next X months/years by starting from observed and collected data. >> >> As far as I know, this kind of forecasting can be realized by time series >> forecasting. In Ignite I see no time series based algorithm. Am I right? >> >> If I'm correct I may use or tensor flow or Deep Java Library. But in this >> case what I don't understand is: where should I use these libraries? Inside >> my thick client microservice or should I write an Ignite plugin in order to >> use the scalability feature provided by Ignite? >> >> Thank you >> >> Angelo >> >
