SSAS Environment or CUBE creation methodology

Question

Though I have relatively good exposer in SQL Server, but I am still a newbie in SSAS.

We are to create set of reports in SSRS and have the Data source as SSAS CUBE.

Some of the reports involves data from atleast 3 or 4 tables and also would involve Grouping and all possible things from SQL Environment (like finding the max record for a day and joining that with 4 more tables and apply filtering logic on top of it)

So the actual question is should I need to have these logics implemented in Cubes or have them processed in SQL Database (using Named Query in SSAS) and get the result to be stored in Cube which would be shown in the report? I understand that my latter option would involve creation of more Cubes depending on each report being developed.

I was been told to create Cubes with the data from Transaction Tables and do entire logic creation using MDX queries (as source in SSRS). I am not sure if that is a viable solution.

Any help in this would be much appreciated; Thanks for reading my note.

Aru

EDIT: We are using SQL Server 2012 version for our development.

Answer 1

OLAP cubes are great at performing aggregations of data, effectively grouping over the majority of columns all at once. You should not strive to implement all the grouping at the named query or relational views level as this will prevent you from being able to drill down through the data in the cube and will result in unnecessary overhead on the relational database when processing the cube.

I would start off by planning to pull in the most granular data from your relational database into your cube and only perform filtering or grouping in the named queries or views if data volumes or processing time are a concern. SSAS will perform some default aggregations of the data to allow for fast queries at the most grouped level.

More complex concerns such as max(someColumn) for a particular day can still be achieved in the cube by using different aggregations, but you do get into complex scenarios if you want to aggregate by one function (MAX) only to the day level and then by another function across other dimensions (e.g. summing the max of each day per country). In that case it may well be worth performing the max-per-day calculation in a named query or view and loading that into its own measure group to be aggregated by SUM after that.

It sounds like you're at the beginning of the learning path for OLAP, so I'd encourage you to look at resources from the Kimball Group (no affiliation) including, if you have time, the excellent book "The Data Warehouse Toolkit". At a minimum, please look into Dimensional Modelling techniques as your cube design will be a good deal easier if you produce a dimensional model (likely a star schema) in either views or named queries.

Answer 2

I would look at BISM Tabular if your model is not complicated. It compresses and stores data in memory. As for data processing I would suggest to keep all calculations and grouping in database layer (create views).

Answer 3

All the calculations and grouping should be done at database level atleast in form of views. There are mainly two ways to store data (MOLAP and ROLAP). Use MOLAP storage model for deal with tables that store transactions kind of data. The customer's expectation from transaction data (from my experience) would be to understand the sales based upon time dimension. Something like Get total sales in last week or last quarter. etc. MDX scripts are basically kind of SQL scripts that Cube can understand. No logic should be there. based upon what Parameters are chosen in SSRS report, MDX query should be prepared. Small analytical functions such as subtotal, average can be dome by MDX but not complex calculations.