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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Im new to oracle multidimensional models I haven’t got an ooap copy of AWM to hand at the moment, but the build log output looks a little more detailed than before, in terms of the level of detail about the steps.

I have worked at Oracle for 17 years working on a wide variety of data warehouse projects both as a consultant and an onsite support engineer. The feature allows DBAs great control over tuning performance since they can add or drop MVs according to the usage patterns they see in the Database without having to alter anything in buikding querying application.

Just to complicate matters, analytic workspaces are themselves stored within LOBs in Oracle relational tables, but the way they are created and maintained is quite different to data in relational tables.

What it has also done though is join to the dimension tables to retrieve the dimension attributes, from speaking to people within the development team I think this is a bullding 11g Ccubes has got now, but that will disappear with subsequent releases, as it should be able to get all the dimensional attribute data from the big, denormalized view over the cube and it’s dimensions.

A First Look at Oracle OLAP 11g

Create relational views of the data. However, if you license the OLAP Option, you now have additional options open to you that can however slightly complicate matters. 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.

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Pressing this brings up the following dialog The message on the dialog reads: The first thing I notice is that the views over the AW dimensions and cube have been built automatically. The Multidimensional ‘OLAP’ database software category largely grew in popularity as a solution to providing fast access to multidimensional data and calculations.

In this case, processing of buildjng cube is localized to the partition with new or updated time periods.

Oracle OLAP

With this new version of AWM, I’m prompted to select a “Cube Type” at the start – I can either create cubes with the 11g metadata format, or the old 10g metadata format for backwards compatibility. This feature is primarily designed to make it very easy to use the cube as a summary management solution for applications that query relational tables.

Right, let’s try again and this time keep an eye out for anything that might generate a cube script.


Oracle decided in the late ‘s that in-database analytics was the way to go, and one of the major engineering projects undertaken was to take all the benefits of multidimensional data types and multidimensional data processing and calculation from the best multidimensional databases, and push it into the kernal of Oracle Database. This tells the OLAP Option to create a variable called “sales,” and dimension it by our geography and products concatenated dimensions.

Oracle OLAP: Creating Cubes with Simple SQL

Level-based Dimensions’ hierarchies are defined by the relationship between levels, and levels map to columns in relational tables. Step 1 is to create SQL dimension objects for each of the dimension tables.

Because the summary has less rows, the result can be calculated faster and query performance is improved. The first step in working with multidimensional datatypes is to create an analytic workspace. Refreshing the OLAP cube can be plugged into exactly the same MV refresh mechanisms used for regular relational MVs, so the cubes can easily slot into existing maintenance procedures. Cube objects are one or more measures, that are dimensioned by by a common set of dimension objects.

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.

I select the percentage difference from prior vuilding calculation, whereapon the dialog changes and reflects the chosen calculation: A single dimension could contain multiple hierarchies and the database could contain multiple dimensions, unique within each schema.

I copied across the relational source data buikding the Global schema on my 10g database not the analytic workspace within it, just the source dataand set up the 11g Global schema with the usual connect, resource etc priviledges, together with these extra ones. Using this presents the opportunity for your regular Oracle DBAs to refresh your AWs in the same way as MVs, and the fact it’s regarded as an MV in the first place opens it up to being used by the query rewrite mechanism, which is where the “speed up relational Discoverer reports” thing comes in – if we can add an AW over some relational data, and register the AW for query rewrite, it might solve the perennial problem of getting relational Discoverer reports to perform well when there’s lots of data and complicated user queries.

Do NOT take anything written here, unless explicitly mentioned otherwise, to be Oracle policy or reflecting Oracle’s support policy. Not only is the calculation of the aggregates very efficient, but the storage, and management of them is too. Oracle OLAP is an separately licensable option of the Oracle database that offers an embedded multidimensional calculation engine within the database.

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Select a buildingg in the navigation tree. The rules sub-tab has the same features as previous AWM versions set the aggregation order and methodhowever the new precompute tab now offers the ability to precompute by levels, as per previous AWM versions, or by percentage of the cube, which is new.

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Regular relational table based MVs have been available in Oracle Database since Oracle 8, and are widely used by BI systems as they simplify the summary management aspects of those systems, and also deliver a feature called query re-write which can improve query performance. For more details on the various OLAP implementations with Oracle 9i and 10g, take a look at these further articles.

This program o,ap 1 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. 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: Interestingly though, bullding I go back into AWM and set the cube refresh method to “Fast” rather than “Force”, which was the original setting, it marks the accompanying materialized view as being unavailable again, which I think is chbes the issue was a minute ago.

Here’s the product dimension one: It combines first class multidimensional data types, and calculation engine with the other cibes, scalability, security, high availability and manageability features of Oracle Database.

Under the list of current steps are three cubrs to create new steps, edit an existing step or delete a step, and pressing the New Step button displays a whole new set of build options.

I have an Oracle 10g Database with relational tables in it. Search BC Oracle Sites. I was especially interested to note a “wait events” script as well – I don’t know whether this had anything to do with it, but I suggested this to the product management llap a similar time ago, I’ll be interested to see what activities and waits this provides diagnostics on. Looking down the compatibility buiilding 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:.

Choose to partition the cube using statistics when the cube is updated across many time periods. 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 buipding actual cube data hasn’t been read in yet. If it works as it should do, it will give the same loap of insight into AW read and build activity that we biilding get when working with the relational part of an Oracle data warehouse.

Now, I start up Analytic Workspace Manager 11g and log in for the first time. 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. It tells AWM to either precompute nothing set it to zero and have all aggregates calculated on bullding fly, set it to and have everything precomputed, or some figure in between.

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