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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Either way, both are accessed using the same Java OLAP API, which in turn decides either to retrieve its data from relational tables or from analytic workspaces, depending on how you’ve chbes the data.

A First Look at Oracle OLAP 11g

llap There’s a star schema with time, product, customer and channel dimension tables and a sales fact table. Search BC Oracle Sites. Oracle Express was originally a product designed and sold by a company called IRI, who sold the technology to Oracle in who then rebadged it and sold it as a specialist OLAP server product for high-end analysis.

This must be like the old Sparsity Advisor in 10gR2, which actually samples the source data and calculates the actual sparsity value for each dimension. Again, not sure how it came to this conclusion, presumably it’s down to the numbers of dimension members in each of the hierarchy levels, as the actual cube data hasn’t been read in yet.

Going back into AWM, bringing up the Cube dialog shows that all of the compatibility checklist is marked with green ticks, but the Ucbes View Details tab shows that this feature hasn’t been enabled. This program will bullding create an analytic workspace, 2 create OLAP dimensions from the SQL dimensions, 3 create a cube from the table-based materialized view and 4 create a cube-organized materialized view on the cube to enable query rewrite into the cube.

In this example, the table-based — materialized view aggregates data from the day to month levels in time — and the customer to city levels in customer. I’m not sure on what basis it picked the “Quarter” level, except perhaps that it’s the middle one between month and year, but I press cancel for the moment and go back to the advisor, this time picking the Statistics option instead.

Cube objects are one or more measures, that are dimensioned by by a common set of dimension objects. With Oracle 9i, is a bit of a complicated task, as it has to be done manually, and consists of a two-step process.


A First Look at Oracle OLAP 11g

I select the percentage difference from prior period calculation, whereapon the dialog changes and reflects the chosen calculation: To create a simple cube that has one buildnig and uses our one dimension, first of all create a table to contain the measure. It continues to query the same underlying relational fract table it ever did, and the Database transparently accesses data from the cubes instead.

It’s not helping me much. I go back to AWM first though, build the cube again, which works ok and I see from SQL Developer that a materialized view has been created for the cube. Oracle OLAP is an separately licensable option of the Oracle database that olaap an embedded multidimensional calculation engine within the database.

Oracle OLAP

On a later date, I’ll be seeing how much it can speed up a series of relational Discoverer workbooks, this for me is the major payoff for this new technology, as it offers two main benefits – the ability to keep on using Discoverer relational or Answers Plus, when it comes out but with the speed and calculation ability of Oracle OLAP. As of Database 11g, the only ‘special’ requirement is that the tool or application has some basic ‘aggregate awareness’ or can be configured that way.

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

If, however, you’re creating multidimensional OLAP dimensions and cubes, and storing them in analytic workspaces, you’ll need to create dimension and variable objects within the analytic workspace, and you’ll either need to manually enable them for the OLAP API if you’re using Oracle 9i, or they’ll be automatically enabled for you, if you’re using Oracle 10g.

In my experience, when you come to aggregate a cube, you don’t really think “what percentage of the cube shall I aggregate”, you really think “how much time shall I allocate to the cube build”, or “how much disk space should I allocate” – if we could aggregate based on the likely amount of disk space a cube will take up something Discoverer Administrator used to do, with it’s Summary Advisor, and Enteprise Manager does I think when recommending MVs to create this would be even more useful.


Going back then to the original question, first of all, if you’re creating relational OLAP dimensions and cubes, you don’t need to create additional tables to hold your data, as your dimensions and cubes are just additional metadata that sits on top of existing tables that is later used by either the query rewrite mechanism, the summary advisor, or by OLAP tools that use the Java OLAP API.

The cube will — use these as the source definitions for cube. Taking a look at the online help, it says the following: So I start off then by creating the CUSTOMER dimension in the Global model, which I create in exactly the same way as with earlier versions, starting first with the dimension, then the levels, then the hierarchies, and finally the mapping.

Anyhoo, it looks like it’s working now, plus buileing also another button just below the MV details panel that launches a Materialized View Advisor. Oracle Database OLAP cubes deliver excellent query performance, which scales well for large numbers of concurrent users.

This is obviously gathering stats on the MV object over the AW dimension so that it gets considered for query rewrite.

Oracle OLAP – Oracle FAQ

It is only available with Oracle Enterprise Edition. 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. A variable is like a fact table with one fact column, and is defined thus:.

Note that in this example the detail of the cube will be a summary of the fact table. This extends the reach of ‘OLAP’ significantly, making it easier to deploy whether for Business Intelligence BIData Warehousing, or for operational applications that are calculation intensive and require fast query response.

If I take a look down at the materialized view section though, I can see MVs for the dimensions, but not for the cube. Your analytic workspace should now be created. As with 8i, you create your tables first, then define your dimensions, which reference columns in the tables. I create it and then map it to the source data, and then switch back to the Materialized View tab for the cube.