After creating cubes, measures, and dimensions, you map the dimensions and . schema following the instructions in Installing the Oracle OLAP 11g Sample. I realize you asked this in August , but in case it still helps you or others, as of Feb , SQL Developer has an OLAP extension which seems to be what. In this course, students learn to progressively build an OLAP data model to support Students learn to design OLAP cubes to serve as a summary management.

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Analytic Workspaces are multidimensional workspaces held within Bbuilding in Oracle tables, that store data using a technology originally introduced with Oracle’s Express line of products.

Oracle OLAP – Oracle FAQ

Next, we need to create some dimensions. Data Warehousing and Business Intelligence professionals happily deployed multidimensional products like Express Server as specialized data marts, despite the obvious architectural drawbacks of the approach, simply because the alternatives were poor. The first thing I notice is that the views over the AW dimensions and cube have been built automatically.

Create some standard Oracle dimensions, which are then used by query rewrite to roll up from one materialized view, at a certain level of aggregation, to another.

A First Look at Oracle OLAP 11g

As with any other materialized view, you will need to make sure all the necessary constraints are in place on the base tables. Query Rewrite allows SQL queries that are requesting aggregate data from a detailed fact table to be automatically re-directed by the Oracle Database optimizer to access a suitable summary table in the database instead. Anyway, I choose to ignore the advice, and leave the cube as compressed, which removes the need to order the dimensions anyway, and instead right-click on the cube and select the Maintain option.

Create relational views of the data. Feel free to ask questions on our Oracle forum.

I wonder what’s going on here then. The first product of that category long ooap the term “OLAP” was coined in the ‘s was an early iteration of what was to become Oracle Express.

This is obviously gathering stats on the MV object over the AW dimension so that it gets considered for query rewrite. Aggregation operator for each dimension must be Sum, Minimum or Maximum presumably, like the compression feature in 10gR1, this will be extended to other aggregation methods in the 11gR2 cubrs Aggregation operator must be the same for all dimensions in the cube probably because the concept of dimensions having different aggregation operators doesn’t really exist in SQL, and therefore MVs Going over to the Partitioning tab in the Cube dialog, I notice a new Partitioning Advisor button: The objective is to create a cube and cube-organized materialized view that manages all summary data beginning at Month, Item, City and Channel levels.


On this tab, you can enable the cube for MV refresh, set the refresh method and refresh mode, and also enable it for query rewrite, so that the analytic workspace is considered when queries come in against the source tables used to populate it. For more details on the various OLAP implementations with Oracle 9i and 10g, take a look at these further articles.

It’s not helping me much. Search BC Oracle Sites.

I select the percentage difference from prior period calculation, whereapon the dialog changes and reflects the chosen calculation: As compression is pretty much a no-brainer from 10gR2 onwards though, that’s not a problem the restriction on types of aggregation that were originally in 10gR1 were lifted with 10gR2and so I press the Advisor button and see what happens: Now cuebs looks cubea a very neat new feature.

Display the Materialized View tab.

A First Look at Oracle OLAP 11g

Here’s the product dimension one: For BI systems that support a varied workload, and one that includes ad-hoc access to the data, the OLAP Oap therefore provides an imaginative alternative.

Looking down the compatibility checklist was quite interesting; apart buulding the obvious ones “dimension must be fully mapped, dimension must have one or more hierarchies” and so on there were some ones I hadn’t expected:. Step 1 is to create SQL dimension objects for each of the dimension tables. I especially like the New Load Step feature, this makes it very clear whether the cube is incrementally loaded or synchronized deleting old data out of the cube was nuilding a headache in previous AWM versions, and most new developers didn’t realize this didn’t happen by default and it strikes me that this is a very nice, very welcome new bit of functionality.


Oracle OLAP

My current role is as part of the Business Intelligence solutions buliding for Oracle EMEA, based in the UK but often sighted at hotels, airports and Oracle customer locations around the region.

The first step in working with multidimensional datatypes is to create an analytic workspace. If it works as it should do, it will give the same sort of insight into AW read and build activity that we currently get when working with the relational part of an Oracle data warehouse.

The dimension itself looks fine: By clicking “Post Your Answer”, you acknowledge that you have read our updated terms of serviceprivacy policy and cookie policyand that your continued use of the website is subject to these policies.

To create materialized views: Invalid materialized view name” Do you have any idea? All of this additional work is carried out for you automatically, when you use the Oracle Enterprise Manager GUI to create your dimension, or you can enter the commands manually, as listed above. Newer Post Older Post Home. The MV over the cube is similarly interesting: My original source data didn’t have these, so adding them is a good idea, although I’d be interested to see how the dimension feature and query rewrite, to think about it works when your querying a full-solved cube, or a partially-solved cube come to think about it.

Oracle in the News Loading This feature is primarily designed to make it very easy to use the cube as a summary management solution for cubds that query relational tables.

WineGoddess 2 9. Looking down the compatibility checklist was quite interesting; apart from the obvious ones “dimension must be fully mapped, dimension must have one or more hierarchies” and so on there were some ones I hadn’t expected: Because the summary has less rows, the result can be calculated ccubes and query performance is improved.