I'm trying to analyse data from my cube from Analysis Services in Tableau. My cube is:
And now I'd like to count number of facts that happened in each state (state is connected through city).But when I'm choosing StateID from DimStates Tableau shows, that data are incompatible. Is there any way to join them? Counting number of facts in each city work well.
Please help
Tableau's functionality is severely limited when working with cube data sources. I can't see exactly what you're trying to do, but it looks like you're trying to add a table calculation to the measure in your view which (depending on the aggregation) isn't supported:
Cube data sources are pre-aggregated and thus do not support aggregation functions, such as SUM(), AVG(), and CNT().
It may be possible to use Table Calculations to perform aggregation operations on the cell-level results from the cube in Tableau.
https://help.tableau.com/current/pro/desktop/en-us/cubes.htm
Related
During the Cloud Migration of an On-Premise Microsoft SQL DB, the OLAP Cube, which is part of it, should also be replaced (but not migrated directly). There is the business requirement to keep the functionality in Tableau that you can select different measurements and dimension with their corresponding aggregations, as is possible now when connecting to the OLAP Cube in Tableau.
The underlying Data Source View includes ca. 10 tables (e.g. customer, sales, payment-method, customer-segmentation, time). So via OLAP the analysis "give me the average sales per payment method per customer-segment for every week" is a couple of clicks, in pure SQL it's already some effort.
How can you offer defined aggregations for some BigQUery tables without the user having to write the joins and aggregations by themselves, mainly because it takes much more time than simply drag & drop (SQL skills & time of query-execution are not the issue)?
The answer turns out to be pretty straight forward:
Join all source data together and write it into one flat table in BigQuery which includes the same information as the data source view in the OLAP Cube. Then Tableau connects to this table. The "measurements" logic from the cube is implemented as calculations in Tableau, the table columns are the dimensions.
Some caution needs to be applied when replicating the measurements because 1:n relations in the Data Source View result in multiplied data in the flat table. This can be solvedwith the correct use of Distinct Functions (e.g. "Distinct Count") in the measurement definition.
The table will end up quite large, but the queries on it are very fast, resulting in a performance increase compared to the OLAP Cube with the same user experience as using a cube in Tableau.
I would like to understand how OLAP-cube operations (i.e. drilling up/down, slicing/dicing and pivoting) and MDX are related.
My current guess is that OLAP-cube operations to MDX are like relational algebra to SQL. However, I do not see how some basic features of MDX correspond to OLAP-cube operations. For example, consider the following query on the demo "Sales" cube that comes with icCube:
SELECT {([Ottawa],[2009]), ([United States],[Feb 2010])} on Rows,
[Measures].members on Columns
FROM [Sales]
How does the use of tuples (e.g. ([Ottawa],[2009])) correspond to an OLAP-cube operation?
Yes, "OLAP-cube operations are what visualization tools are expected to implement". MDX is the query language that is executed against a cube that produces a result. OLAP clients generally run MDX against a cube. "OLAP cube operations" as described in that wikipedia are usually as a result of a person performing adhoc analysis against a cube in an client application.
Cube provide a structure and an access language that usually makes these types of operations easier (or at least faster)
How does MDX relate to a "drill down" operation? for example?
Firstly some MDX has already been run and yielded some kind of view of the cube (generally some rows, some columns, and a measure in the intersection although the MDX language syntax doesn't limit to two axes only).
So a person sees this information and decides to drill down on a single item in the row (this item was previously returned by some MDX). So the OLAP client generates some MDX that provides the drilled down view on the item
It might just add a children MDX function to the item in question. Or it might do it some other way. It depends on the client.
Heres some introductory info on how you can eavesdrop on the interactions between an OLAP client (which one? doesn't matter) and a SSAS cube
https://learn.microsoft.com/en-us/sql/analysis-services/instances/introduction-to-monitoring-analysis-services-with-sql-server-profiler
You can think of the MDX query specifying areas or regions of space within a cube - the tuple is a primary way of giving the processor co-ordinates which correspond to the part of the cube you are interested in.
It is the intersection of the co-ordinates and slices you specify that give you a result.
MDX is strongly related to set theory as the main types withing the cube are dimensions, sets, tuples, members etc.
An MDX query defines a table and for each of the table cell we've a tuple. In your scenario assuming we've two measures ( Meas1, Meas2 ) :
([Ottawa],[2009],[Meas1]) ([Ottawa],[2009],[Meas2])
([United States],[Feb 2010],[Meas1]) ([United States],[Feb 2010],[Meas2])
On this cell tuples you might add the WHERE clause, the SubQuery and the defaults that might be different than ALL (not advised). Remember all is a 'special' member that is ignored.
A tuple defines a single measure, Meas1 or Meas2, this will select the 'fact table' with a measure column, usually a numerical value. The other members are used to select rows in the table performing on them the aggregation defined by the measure ( sum , min, max.... ) on all rows defined by the tuple members, Ottawa and 2009 for example. As whytheq explains, you've a lot of transformations to 'play' with members as you would with sets.
This is the simple vision, as you can use calculated members that define a transformation instead of a simple row aggregation (e.g. Difference with previous year) and some aggregations are a bit more tricky ( open, close...).
But if you understand this well you got a perfect ground to understand MDX.
We are migrating from Microsoft's Analysis Services (SSAS) to HP Vertica database as our OLAP solution. Part of that involves changing our query language from MDX to SQL. We had a custom MDX query generator which allowed others to query for data through api or user interface by specifying needed dimensions and facts (outputs). Generated MDX queries were ok and we didn't have to handle joins manually.
However, now we are using SQL language and since we are keeping data in different fact tables we need to find a way how to generate those queries using same dimension and fact primitives.
Eg. if a user wants to see a client name together with a number of sales, we might take a request:
dimensions: { 'client_name' }
facts: { 'total_number_of_sales' }
and generate a query like this:
select clients.name, sum(sales.total)
from clients
join sales on clients.id = sales.client_id
group by 1
And it gets more complicated really quickly.
I am thinking about graph based solution which would store the relations between dimension and fact tables and I could build the required joins by finding shortest path between the nodes in a graph.
I would really appreciate any information on this subject including any keywords i should use searching for a solution to this type of problem or 3rd party products which could solve it. I have tried searching for it, but the problems were quite different.
You can use free Mondrian OLAP engine which can execute queries written in the MDX language on the top of relational database (RDBMS).
For a reporting you can try Saiku or Pentaho BI server on the of Mondrian OLAP.
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.
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.
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).
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.
I am trying to learn aggregation in SSAS,
I belive when we create a cube it generates dimensions and facts it generates all the possible combinations already then,
What exactly happens when we create Aggregation in SSAS?
Please Help
Generally not all aggregations are created, but instead the "SSAS engine creates the aggregations based on an internal cost-vs.-benefit algorithm." Think of the aggregations as group by's in SQL that are saved on disk, as defined by your aggregation design.
If all possible aggregations are saved we can get database explosion causing the aggregations to be larger than the source data. This can actually make query performance worse.