OLAP using Cassandra and Spark: http://www.slideshare.net/EvanChan2/breakthrough-olap-performance-with-cassandra-and-spark
What is the cardinality of your cube dimenstions? Obviously any multi-dimensional data must be flattened. Cassandra tables have fixed named columns, but... the map datatype with string key values effectively gives you extensible columns. -- Jack Krupansky On Tue, Mar 1, 2016 at 11:22 AM, Andrés Ivaldi <iaiva...@gmail.com> wrote: > Jonathan thanks for the link, > I believe that maybe is good as Data Store part, because is fast for I/o > and handles Time Series, for analytics could be with Apache Ignite and/or > Apache Spark > what it worries me is that looks very complex create the structure for > each Fact table and then extend > > regards. > > On Sun, Feb 28, 2016 at 12:28 PM, Jonathan Haddad <j...@jonhaddad.com> > wrote: > >> Cassandra is primarily used as an OLTP database, not analytics. You >> should watch this 30 min video discussing Cassandra core concepts (coming >> from a relational background): >> https://academy.datastax.com/courses/ds101-introduction-cassandra >> >> On Sun, Feb 28, 2016 at 5:40 AM Andrés Ivaldi <iaiva...@gmail.com> wrote: >> >>> Hello, At my work we are looking for new technologies for an Analysis >>> Engine, and we are evaluating differents technologies one of them is >>> Cassandra as our Data repository. >>> >>> Now we can execute query analysis agains an OLAP Cube and RDBMS, using >>> MSSQL as our data repository. Cube is obsolete and SQL server engine is >>> slow as data repository. >>> >>> I don't know much about cassandra, I read some books, and looks to fit >>> well on what we are needing, but there are some things that looks like a >>> problem for us. >>> >>> Our engine is designed to be scalable, flexible and dynamic, any user >>> can add new dimensions or measures from any source, all the data is stored >>> on Cube(this is fixed data) and MSSQL(dynamic data) so we have decoupled >>> tables with the dimension values. >>> >>> >>> Ok, with the context given I'll like to clear some doubts >>> >>> - I able to flat the table with all the possible dimension values to >>> cassandra, creating the pk against the dimension columns? this will give me >>> the "sensation" of data pivot over the PK columns? If correct, what if I >>> want to select the order of the columns, or add another or reduce them? >>> - It's possible to extend the values of a row dynamically? What we do >>> often is join row against a value of a mapped external data value to extend >>> the dimensions hierarchical value structure (ie state->Country->Continent) >>> >>> I know we can do some of this things in the core of our engine, like the >>> dimension extension of the values or reduce columns, but as we are >>> evaluating differents technologies is good to know. >>> >>> Regards!! >>> >>> >>> -- >>> Ing. Ivaldi Andres >>> >> > > > -- > Ing. Ivaldi Andres >
