I forgot to mention that there is also Zeppelin and jove-notebook but I haven't got any experience with those yet.
*Irfan Ahmad* CTO | Co-Founder | *CloudPhysics* <http://www.cloudphysics.com> Best of VMworld Finalist Best Cloud Management Award NetworkWorld 10 Startups to Watch EMA Most Notable Vendor On Wed, Mar 18, 2015 at 8:01 PM, Irfan Ahmad <ir...@cloudphysics.com> wrote: > Hi David, > > W00t indeed and great questions. On the notebook front, there are two > options depending on what you are looking for. You can either go with > iPython 3 with Spark-kernel as a backend or you can use spark-notebook. > Both have interesting tradeoffs. > > If you have looking for a single notebook platform for your data > scientists that has R, Python as well as a Spark Shell, you'll likely want > to go with iPython + Spark-kernel. Downsides with the spark-kernel project > are that data visualization isn't quite there yet, early days for > documentation and blogs/etc. Upside is that R and Python work beautifully > and that the ipython committers are super-helpful. > > If you are OK with a primarily spark/scala experience, then I suggest you > with spark-notebook. Upsides are that the project is a little further > along, visualization support is better than spark-kernel (though not as > good as iPython with Python) and the committer is awesome with help. > Downside is that you won't get R and Python. > > FWIW: I'm using both at the moment! > > Hope that helps. > > > *Irfan Ahmad* > CTO | Co-Founder | *CloudPhysics* <http://www.cloudphysics.com> > Best of VMworld Finalist > Best Cloud Management Award > NetworkWorld 10 Startups to Watch > EMA Most Notable Vendor > > On Wed, Mar 18, 2015 at 5:45 PM, davidh <dav...@annaisystems.com> wrote: > >> hi all, I've been DDGing, Stack Overflowing, Twittering, RTFMing, and >> scanning through this archive with only moderate success. in other words >> -- >> my way of saying sorry if this is answered somewhere obvious and I missed >> it >> :-) >> >> i've been tasked with figuring out how to connect Notebook, Spark, and >> Accumulo together. The end user will do her work via notebook. thus far, >> I've successfully setup a Vagrant image containing Spark, Accumulo, and >> Hadoop. I was able to use some of the Accumulo example code to create a >> table populated with data, create a simple program in scala that, when >> fired >> off to Spark via spark-submit, connects to accumulo and prints the first >> ten >> rows of data in the table. so w00t on that - but now I'm left with more >> questions: >> >> 1) I'm still stuck on what's considered 'best practice' in terms of >> hooking >> all this together. Let's say Sally, a user, wants to do some analytic >> work >> on her data. She pecks the appropriate commands into notebook and fires >> them >> off. how does this get wired together on the back end? Do I, from >> notebook, >> use spark-submit to send a job to spark and let spark worry about hooking >> into accumulo or is it preferable to create some kind of open stream >> between >> the two? >> >> 2) if I want to extend spark's api, do I need to first submit an endless >> job >> via spark-submit that does something like what this gentleman describes >> <http://blog.madhukaraphatak.com/extending-spark-api> ? is there an >> alternative (other than refactoring spark's source) that doesn't involve >> extending the api via a job submission? >> >> ultimately what I'm looking for help locating docs, blogs, etc that may >> shed >> some light on this. >> >> t/y in advance! >> >> d >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/iPython-Notebook-Spark-Accumulo-best-practice-tp22137.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >> >
