One additional comment I would make is that you should be careful with
Updates in Cassandra, it does support them but large amounts of Updates
(i.e changing existing keys) tends to cause fragmentation. If you are
(mostly) adding new keys (e.g new records in the the time series) then
Cassandra can be excellent

cheers


On Wed, Feb 11, 2015 at 6:13 PM, Paolo Platter <paolo.plat...@agilelab.it>
wrote:

>   Hi Mike,
>
> I developed a Solution with cassandra and spark, using DSE.
> The main difficult is about cassandra, you need to understand very well
> its data model and its Query patterns.
> Cassandra has better performance than hdfs and it has DR and stronger
> availability.
> Hdfs is a filesystem, cassandra is a dbms.
> Cassandra supports full CRUD without acid.
> Hdfs is more flexible than cassandra.
>
> In my opinion, if you have a real time series, go with Cassandra paying
> attention at your reporting data access patterns.
>
> Paolo
>
> Inviata dal mio Windows Phone
>  ------------------------------
> Da: Mike Trienis <mike.trie...@orcsol.com>
> Inviato: ‎11/‎02/‎2015 05:59
> A: user@spark.apache.org
> Oggetto: Datastore HDFS vs Cassandra
>
>   Hi,
>
> I am considering implement Apache Spark on top of Cassandra database after
> listing to related talk and reading through the slides from DataStax. It
> seems to fit well with our time-series data and reporting requirements.
>
>
> http://www.slideshare.net/patrickmcfadin/apache-cassandra-apache-spark-for-time-series-data
>
> Does anyone have any experiences using Apache Spark and Cassandra,
> including
> limitations (and or) technical difficulties? How does Cassandra compare
> with
> HDFS and what use cases would make HDFS more suitable?
>
> Thanks, Mike.
>
>
>
> --
> View this message in context:
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