Hi Niclas, thank you - I will take a look on the entity store cassandra implementation.
Cheers, Jiri 2016-02-21 1:52 GMT+01:00 Niclas Hedhman <[email protected]>: > About Cassandra... > > I think the only reason was that with CQL no one took the time to refactor > the code, perhaps due to some conceptual changes were introduced. > But it could have been that there were no true test suite, and failing the > Release Criteria and fixing "run embedded during test" with the same client > as in production code, may have been non-trivial (not sure). You have old > code in the sandbox. > > Useful link; > > http://prettyprint.me/prettyprint.me/2010/02/14/running-cassandra-as-an-embedded-service/index.html > But I am uncertain if it is still relevant. > > Sandbox; > > https://github.com/apache/zest-sandbox/tree/master/extensions/entitystore-cassandra > > Niclas > > On Sun, Feb 21, 2016 at 3:38 AM, Jiri Jetmar <[email protected]> > wrote: > > > Hi guys, > > > > what is the status of the Apache Cassandra Entity Store ? Somehow I can > > remember that Cassandra was supported but can not > > find it in the current development branch. > > > > The reason I;m asking is because Cassandra works well with the analytical > > Apache Spark stack. > > > > Assume a scenario where you have e.g. the following Domain Models like : > > > > - Products > > - Orders > > - Users > > > > Each Domain has its own Api, Usercases and States that is stored in the > > DM. Now you have e.g. a Webshop UI on top of the > > above Domains. > > > > Now you want to answer questions like : What kind of Users are buying > > Product X. Or, find those Users that are most likely buying > > Product X in the next Y days. > > > > To answer those questions is typically a challenge of "Data Analytics" > > using algorithm like PCA, Random Forest, Regressions, XGBoost, etc. > > All can be done surely in Java, but from my impression the Python > community > > built over the last years an amazing tool set and environments. > > > > Also a "Data Scientist" has to try out different things, until a good > and > > robust prediction is done. So the workflow is interactive and here is > where > > Apache Spark is offering > > great tools, including the usage of the IPython/Jupyter Notebooks. > Another > > benefit is that one does not need to kick-on any ETL Jobs to transfer the > > transactional data from the Domain Models to the analytical world - > > Cassandra does this already. So one can do all the analysis on a realtime > > snapshot > > without influencing the transactional processing. > > > > Thank you. > > > > Cheers, > > Jiri > > > > > > -- > Niclas Hedhman, Software Developer > http://zest.apache.org - New Energy for Java >
