Archive for the ODI Architecture Category

KScope14 presentation: Unleashing Hyperion Planning Security Using ODI

Posted in Hyperion Planning, Kscope 14, KScope 15, ODI, ODI Architecture, ODI Architecture with tags , , , , , on July 14, 2014 by Rodrigo Radtke de Souza

Hi all!

We are back from KScope14! What a great conference! KScope is indeed the world’s greatest EPM conference ever! It’s the best place to talk to the best EPM professionals, learn about cutting edge technology and, in our case, also present some of the work that we are currently working on 🙂

Here is the PDF containing our session at KScope14. Please feel free to write comments about it! It’s always great to receive some feedback!

KScope 14 Presentation

Hope you enjoy it! If you were not able to go to KScope14 this year, please make an effort to go next year (it will be held at Hollywood!!! Kscope15). It’s worth every dollar invested!

See ya!



10 Important Things to Improve ODI Integrations with Hyperion Planning Part 9 (Generic Data Load)

Posted in EPM, Hyperion Planning, ODI, ODI Architecture with tags , , , , , on April 25, 2014 by Rodrigo Radtke de Souza

Hi all, today let’s see how we may create dynamic data load components for any number of Hyperion Planning applications. As you know, all data related to Hyperion Planning actually resides in an Essbase database and ODI has a Knowledge module that loads data into it called “IKM SQL to Hyperion Essbase (DATA)”. In this post we will tweak this Knowledge Module allowing it to read the Planning Metadata repository and dynamically build its own data store mappings and definitions depending on which Hyperion Planning application it will load data to. But first let’s take a look on how the original “IKM SQL to Hyperion Essbase (DATA)” works:


As you can see, it is a fairly simple KM with two main steps: “Prepare for loading” and “Load data into Essbase”. If you open “Prepare for loading” step you will see some Jython code there getting all configurations and options to be used in your data load and since our objective here is to make it dynamic to load any Hyperion Planning application we should focus on the bellow settings:


These settings are getting all the connection information related to the target data store in the Interface ODI component. If you already worked with Hyperion Planning and ODI, you know that the target data store contains all dimensions from a particular Hyperion Planning application and a column named “Data” that will contain the value for that particular intersection. This data store belongs to a data model, which is connected to a logical schema that is tied (by a context) to a certain physical schema that contains the application server/user/password/application/database.

In this example we will consider that we have five Hyperion Planning applications and all of them resides in the same server and uses the same Essbase username and password to connect to them, but if your architecture is different from that, please feel free to customize this information as well. In order to make this KM dynamic, let’s add two more options to it called “APPLICATION” and “CUBE” and modify the application and database information code to get their value from options instead of the target data store, like this:


These two options will contain two ODI variables that will indicate which application/cube you will be loading. Keep those two variables in mind, as we will talk more about those later in the post. Now if you open “Load data into Essbase” step you will see that this one is responsible to actually fetch the data from a table based in the source and target data store columns and mappings as below:


If we want it to load any Hyperion Application, we need to change this to not rely on the target data store (that is a static information) in order to know which dimensions to load for a specific application. If you are a reader of this blog, you probably already know where the answer to this problem relies, right? If you thought about Hyperion Planning metadata repository, you are right! Let’s change this KM to read the metadata repository, figure out which dimensions that application has and load data to it. First let’s create another option in this KM called “PLANNING_SCHEMA”, which will contain the actual database schema name where the Hyperion Planning application resides in Oracle. Then we will add the following query in the source tab of “Load data into Essbase” step:


Befire we continue, this command LISTAGG only exists in Oracle DB 11.2 or newer. If you DB is in an older version please try the WM_CONCAT command. This is a undocumented command but works very well. You need to change the SQL projection from:




Also you will need to create and add to this source tab a logical schema that points to the database that contains the Hyperion Planning repository. One very important thing about this technique: it only works when you have all Hyperion Planning applications repository in a single database because even though the schema name will be changed dynamically, giving us the possibility to load any number of applications with a single component, the connection to the metadata database will be a single one. Of course that you can further change your process to accept dynamic topology information as seen in our previous post here, but in this example we will keep things simpler to demonstrate the technique. After you add this query in source, it’s just a matter to change the target code to something like this:


Our source query will retrieve one row only (called #MAPPING) with the exact number of dimensions that one specific planning application has (based on “PLANNING_SCHEMA” option) and will pass it to the target tab, where it will be used to retrieve the data. But you may be thinking now…. What about the source table for this data load? It may have different formats, different names, and different sources… it will not work in a dynamic load component…. and you are right, it won’t, so that’s why we need something extra to make this component work: an INBOUND_GENERIC table.

INBOUND_GENERIC is a table that contains all possible columns (and each column is a planning dimension) for all Planning applications that you will need to load. For example: if you need to load five applications and together they have 15 distinct dimensions, you will have an INBOUND_GENERIC table with 15 columns (one for each dimension name) plus three auxiliary columns: APP_NAME (the Hyperion Planning application name), CUBE (the cube that you will load data within that application) and INTERFACE_NAME (that is the job name that is loading that data). These three columns give you the flexibility to have in the same table information about different applications, cubes and even inbound jobs! For example, if you have only one application, but contains several inbound jobs, you still may have one single Essbase load component for your entire architecture, saving you time for any maintenance or new development. Also a good performance trick here is to have this table partitioned by INTERFACE_NAME, so you may easily truncate the partition before any new inbound job runs (and even parallel inbound executions).

Also you may be thinking now: ok, I have 10 inbound jobs for 3 different applications. I may create this INBOUND_GENERIC table and load all data to it and have it loaded by this generic Essbase load component, but I’ll still need to create 10 inbound interfaces to load the INBOUND_GENERIC table, right? Yes, you will, but it is worthy. Here are some good points of this architecture:

  • You will have only one single component that loads data to Essbase and all the possible future maintenance will be done in just one point, not in multiple;
  • Since it is modular, it can be added to any other inbound interface easily;
  • ODI constraints work great in this architecture. Think about it: you may have one constraint for each column and you may validate it against the planning metadata repository in order to check if all data is correct before loading to Essbase, which means no more “Unknown Members”, see our post about it here;

After those changes, you should end up with an interface more or less like this:


It doesn’t matter what you do in your mapping here because the KM will build the columns based in the planning metadata repository. Also is important to notice that no transformation will be done in this component, so be sure to do you ETL when you load INBOUND_GENERIC table. In resume, this component considers that the data is already validated, transformed and ready to be consumed by Essbase. The filter here contains which application/cube/interface you are currently loading.

Now it is just a matter to add this interface to an ODI scenario and add the following ODI input variables to it:

  • APP_NAME: Planning application name;
  • CUBE: Planning cube/database name;
  • PLANNING_SCHEMA: Oracle schema name which contains the Planning Metadata installation;

Then you may loop this scenario for any number of applications as you may see in our post about it here.

Thanks everyone! I hope you have enjoyed it!

We’ll be at KScope 14

Posted in EPM, Hyperion Planning, Kscope 14, ODI, ODI Architecture, ODI Architecture with tags on March 18, 2014 by RZGiampaoli

Hi guys how are you doing? We are very happy and proud to announce that our article “Unleashing Hyperion Planning Security using ODI” was approved for KScope 14! We’ll be there to talk about one of our preferred subject areas: How to improve the EPM tools potential using ODI. We’ll be very pleased if you guys show up in our presentation. It’ll be great to find everyone there and talk about EPM and other cool stuffs.

In our session we’ll show some tricks to unleash the planning security and enable it to be create at cell level, automaticaly and using any attribute dimension or UDA.

You can expect a lot of technical informations, a new way to view and work with Hyperion Planning and ODI plus some real facts about what has happened after a successful implementation of this approach.

And remember, if you sign up by March 25th you’ll take advantage of the Kscope early bird rates, then don’t waste more time and let’s be part of the greatest EPM event in the world.

Thank you very much everybody and we’ll be waiting for you in Kscope 14.

Kscope 14 Speaker

10 Important Things to Improve ODI Integrations with Hyperion Planning Part 8 (Building Planning DataStore)

Posted in EPM, Hyperion Planning, ODI, ODI Architecture, ODI Architecture, ODI Mapping on March 10, 2014 by RZGiampaoli

In order to create a process that is able to load any application and dimension using one single ODI interface we need to make some code changes to the KM that is responsible to load metadata into Hyperion Planning. First, we need to understand the ODI concept of a KM. KM is a set of instructions that will take the information from what exists in the source and target data stores of an ODI interface and construct a SQL command based in those data stores. In a nutshell the ODI KM is code generator based in the information that you set in the interfaces, data stores, topology and so on.

As we know the default Hyperion Integration KM is able to load only one application and dimension at a time because of the need of a target data store for each dimension in each application. If we take a deeper look in the KM to see what it does behind the scenes we will see something like this:
KM Behind the ScenesFigure 1 – KM behind the scenes.

Basically what the KM does is translate the Planning application data store to a SQL query, and as we know, we get this data store by reversing a Planning application inside ODI. Fair enough, but this also means that if we could somehow have the same information that ODI has to reverse this application dimension to a data store we could easily end up with the same SQL created from that data store information. As we already showed before we have the Planning application repository itself where all the information about a Hyperion application is stored. We only need to read this information to get the same information provided by the ODI data store.

Knowing this the only thing left is to change the default KM according to our needs, and for this we need to make three changes on it:

  • Make the application name that it is going to be loaded dynamic;
  • Make the dimension name that is going to be loaded dynamic;
  • Change the way that the KM builds its SQL command that will load metadata to Hyperion Planning. Currently it builds its SQL command based on the source and target data stores and the interface mappings;

Default KM Behind the ScenesesFigure 2– Default KM behind the scenes.

In Figure 2 we can see how a default planning integration KM works. Basically it has two main steps: “Prepare for loading” and “Load data into planning”. The first one is responsible to set all information regarding connections, log paths, load options and so on. The second step is responsible to retrieve all source data based in the interface mapping and the source/target data store and load it to planning. In our case, the application and dimension names resides on the first step and the SQL command resides in the second step so we already know where we need to change the code.

But we need to analyze further to know what exactly we need to change. For the application name ODI gets it from “<%=snpRef.getInfo(“DEST_CATALOG”)%>” API function that returns the application name based in the destination target store that is connected to a logical schema that finally resolves into a physical schema that contains the application name itself. If we change it to an ODI variable we will be able to encapsulate this interface into an ODI package and loop it passing the application name as a parameter, making it independent of the target data store topology information and giving us the a ability to load any Hyperion planning application using one single interface.

The dimension name follows the same logic: ODI gets it from “<%=snpRef.getTargetTable(“RES_NAME”)%>” API function that returns the resource name from the target data store that in this case is the dimension name itself. Again if we changed it to an ODI variable we will be able to encapsulate this interface into an ODI package and loop it passing the dimension name as a parameter, making it independent of the target data store resource name and enabling us to load any dimension with one interface.

The third part is the most complex one. ODI data stores for planning applications are so different from one dimension to another that they require one data store object for each dimension. In figure 10 we can see that ODI relies on “odiRef.getColList” API command to return all mappings done in the target dimension data store, which has the correct dimension format required to load that dimension metadata into planning.

So the big question is: How can we change the “Load data into planning” step to use a dynamic SQL to create dynamic interface mappings to load to any application/dimension? The answer is to rely again on the “Command on Source/Target” concept and on the planning repository metadata information.

Instead of getting the mapping information from the ODI data store object, we can query Planning repository to get the same mapping for all dimensions and applications being loaded. The result of this query is a formatted mapping, identically of what ODI would have generated if we used the default planning development, but with the big advantage of being entirely dynamic to any application and dimension.

Dynamic KM behind the scenes
Figure 3 – Dynamic KM behind the scenes.

In figure 3 we can see an example using an Attribute dimension. The command on source will query HSP_OBJECT and HSP_ATTRIBUTE_DIM of a given application (defined by #SCHEMA_APP variable) to retrieve information about one attribute dimension (defined by #DIMENSION variable). Those variables are passed from an external ODI package that will be used to loop all applications and dimensions that we want to load.

Dimension Datastore Information

Table 1 – Dimensions Data Store information.

If we take a further look into all different data stores that a Planning application could have, we will see a pattern regarding the information that we need to send to Planning to load metadata depending of each dimension, as we can see in the Table 1.

The logic to create the dynamic mapping columns is exactly the same used to create the inbound and the extract tables. The only difference is that for the inbound and extract tables we need to put all columns together and for the KM mapping we need to, depending of the selected dimension, take the right information in the application repository. This information will help us to create the necessary mapping that contains the right source columns and the right alias of those columns, which will inform Planning about what that metadata column stands for.

Since our metadata tie out table contains standard columns for all dimensions we don’t need to worry about adjustments when we change to another dimension, and since our source metadata table already has the metadata information in the correct planning format, we don’t even need any kind of transformation here, it is just a matter to read from the metadata source table and load directly to Planning.

In the Figure 3 example we will use the SRC_MEMBER, SRC_PARENT and SRC_ALIAS as the mapping columns and for the Planning alias the only one that is dynamic is the member name alias that identifies the dimension name. To get this information we need to query the application repository looking for the attributes into HSP_ATTRIBUTE_DIM and for its name in HSP_OBJECT table, and finally we can use the OBJECT_NAME column to get the dimension name alias.

Executing this query we will get a one line mapping string that will be passed as a parameter (#MODEL) from “Command on Source” to “Command on Target” and will enable ODI to load metadata to that specific dimension/application. If we execute this interface and look at the query created in ODI operator we will see that the result is the same as a default KM would create but with the big advantage of being entirely dynamic. Following this logic, we would only need to change the value of the #SCHEMA_APP and #DIMENSION variables to get another application\dimension loaded.

Off course we need to work a little more to get the mapping for the other dimensions as Account or Entity, but the idea will be always the same: query the application repository to get the data store information depending on the dimension\application selected.

Dimensions Mapping informationTable 1 – Dimensions Mapping information

In table 1 we can see all the possible mapping combination that we can have in a planning application for the mainly planning dimensions and we notice that some information are dynamic (dependent of the planning repository) and some are fixed. To put everything together in one single query here are some tips:

  • The majority of the columns are fixed and can be obtained with a simple “select ‘Any string’ from dual”;
  • The easiest way to create this SQL is to create separated SQLs for each different kind of information and put everything together using Unions statements;
  • Split the final query in small queries to get the different categories presented in table 1;
  • Use the MULTI_CURRENCY column in HSP_SYSTEMCFG table to find out if that application is a multicurrency one or not;
  • For aggregations and plan type mapping we need to get the name of the plan type itself and for this we use the HSP_PLAN_TYPE table;
  • When the query is ready you need to add a filter clause to filter the dimension from where that information belongs;

With the query ready the only missing step is to insert it into the “Command on Source” tab inside the Planning IKM and pass the string generated by it to the “Command on Target” tab as we can see in the figure 3.

This ends all the preparations that we need for learn how to build a ODI interface that will dynamically load metadata into any number of Planning applications.

Thanks you and see you in the next post.

ODI 12c: excelent for OGG, bad for EPM

Posted in EPM, ODI, ODI 12c, ODI Architecture with tags , , , on November 13, 2013 by Rodrigo Radtke de Souza

Hi all,

Yesterday there was the Oracle’s Webcast “Introducing Version 12c for Oracle Data Integration”. Since our first impression was not so good, we decided to participate in this webcast to see if we could find some new information that would change our minds. Everything was fine as Oracle representatives were talking about the nice new cool features, in especially for Oracle Golden Gate that is more integrated with ODI then never and it actually looks really nice. But then there was one question from someone in the audience that caught our eyes:

Can I integrate this with Hyperion and Discoverer?

“ODI 12c is integrated with OBIEE at the metadata level, we provide capabilities such as report to source data lineage. Hyperion connectivity to applications such as Planning or Financial Management will come in future versions of ODI 12c”

ODI 12c does not support Hyperion. What? What do you mean? At first I thought that something was wrong. Maybe the guy who answered the question did not get it or misunderstood it, so I asked a question:

In one of the answers, you said the following: “Hyperion connectivity to applications such as Planning or Financial Management will come in future versions of ODI 12c.” Does it mean that ODI 12c does not have any more Hyperion Planning KMs for example?

“We are planning on adding support for Hyperion applications in a future release of ODI 12c.”

I couldn’t believe what I was reading. They have done great enhancements on ODI to better support Golden Gate, Big Data, parallel executions, debugger, security stuff BUT they removed Hyperion support from ODI 12c. They just removed. In my last attempt to see if they were not misunderstanding me, I asked again almost the same question:

We work with ODI 11g integrating several Hyperion products, such as Essbase, Planning and HFM. Does it mean that we are not able to migrate to ODI 12c for now?

“We will be adding support for Hyperion applications in future releases of ODI 12c. For now you can still continue to work with ODI 11g when Hyperion connectivity is a requirement.”

Well, that was it. Oracle has stated for all presents there that ODI 12c does not fit to EPM users. They have removed a key functionality from its main ETL product. Do you work with Hyperion? Please, just sit on the corner and wait for some next release. There is no more to say about it. Uninstalling it right now and pretend that Oracle did not launch ODI 12c.

ODI 12c First impressions

Posted in EPM, New Features, ODI 12c, ODI Architecture, ODI Architecture, ODI Mapping with tags , , , on October 26, 2013 by RZGiampaoli

When I started working with ODI it was in version 10, and back there we had a few bugs, the UI was good (back there we could change the expressions and we didn’t have to take out the focus to save the changes, for example) , everything worked well, we could write a variable name with upper or lower case, the metadata navigator worked very well and that was one of the things that made the users choose ODI instead of power center Informatica, because they had an easy way to run their interface at will, and some other good things. It was a very stable version of ODI, good times.

Then 11 version came out. Well, the first thing we noticed was the UI, and the huge amount of bugs that came with it, and most of them in the interface screen. In 11 version, if you try to delete a filter, all the other filters disappears, but they are still there, if you close and re-open the interface they’ll come back, if you change a expression and doesn’t remove the focus from the filed it’ll not commit the changes, if you delete a datastore and put a new one (because some model changed for example) you have a good chance to not be able to save the interface for some bizarre error and you need to do this operation over and over, the variables name must be upper case for some odd motive, and other things. Another big loss was the metadata navigator that was replaced by ODI Console, a worse version with so many bugs that we had to stop using it. Some bugs like lack of security (everybody could see everything), all the execution ran as supervisor, we couldn’t see the load plans (only place where the security works), we couldn’t see the variables and lots of other things.

BUT despite of that, the functionalities for the DEV team were almost the same.

Now we have a new version of ODI. The 12c. Ok, this is only our first impressions and we could have been doing something very wrong (and I pray for this). When a software changes its version and two specialists takes more than 30 minutes trying to figure out why and how or what they need to do to sum a column in an interface or should I say mapping (yes, this is the new name, I liked it and this is one of the few things that made sense to me in this new version), something is very wrong with it.

Ok let’s start from the beginning. When I started to work with datamart, the first tool that I used was OWB, and after some time when I started to use ODI to make some integration, I really missed some stuffs from OWB. It makes sense to get this two tools and merge it together. From OWB we had a cool mapping process that makes easy to understand what that transformation is doing, multiple targets, and a few other things that I missed in ODI, BUT, ODI has the Agent that allow us to connect anywhere without the need to create a heterogeneous service or a dblink or other stuff like that, it has more flexibility (and when I say more you can understand infinite more), we don’t need to deploy the mapping to create a procedure in a oracle database to integrate something, what makes the development test super-fast, we have a lot of components, well, ODI is so much better in this aspect that the few things I missed doesn’t bother me at all.

So in this new version they tried to merge the two tools. What looks good in the paper (I mean blogs and documentations) looks terrible in ODI.

We installed it this weekend to see what happened in this new version and we saw a very different workspace for the interface, I mean for the mappings. This simple ODI UI…

ODI 11g Interface UI

ODI 11g Interface UI

Turned into this:

ODI 12c Interface UI

ODI 12c Interface UI

Humm looks good right? Well, yes and no. I’m working in a 60” full screen TV and I need to drag the screen left and right, up and down to make everything visible, poor devs that has a screen smaller than mine.

Ok but this is only layout, everything else should be better right? Well, unless we were doing something very wrong they put a lot of more complexity to solve some issues that we were able to solve very quickly since version 10.

First of all, in all the other versions if you want to sum something in ODI you just need to get the expression in the target datastore and put a SUM() function on it. ODI would do the group by for you and everything is ok.

ODI 11g Sum

ODI 11g Sum

In the new version you need to drag an object called Aggregate, put all the columns that you want to map trough this (like in OWB), change the options of this object, and in the end, put the same SUM() expression in this object instead of in the target datastore.

ODI 12c Sum Part 1

ODI 12c Sum Part 1

ODI 12c Sum Part 2

ODI 12c Sum Part 2

ODI 12c Sum Part 3

ODI 12c Sum Part 3

If you try to put the expression as before (<=11g) it’ll not create the group by and you’ll not be able to run the interface because it will just simply fail ….

Well, at least in OWB when you use the Aggregate object it’ll aggregate the columns that you define without the need to write the SUM() function. Why they put this new complexity? Ok you can execute this SUM() in another place different from the source or the target but still…

We have some other components that we need to use in the interface right now.

ODI 12c Mapping Components

ODI 12c Mapping Components

The dataset is used when you want more than one dataset in the source (we already have it in the 11g, the difference now is that you need more screen to manage it but the bright side is that you’ll not forget to change anything because you missed the datastore tab like in the old version [yes I did it a lot])

ODI 11g Datastore

ODI 11g Datastore

ODI 12c Datastore

ODI 12c Datastore

The distinct component does not need to be explained, the only thing I had to say is that in the old versions you need only to flag it in a simple check box and now we need to add a distinct component in the flow, drag all columns to it and them drag those columns again to the target. A complete waste of time.

ODI 11g distinct

ODI 11g distinct

ODI 12c Distinct

ODI 12c Distinct

The expression… well, almost the same as Aggregate. Now instead of just write any expression in the target datastore you may add this object in the flow, BUT it will work if you just write the expression on the target. So, why do we need to have this additional object???

ODI 11g Expression

ODI 11g Expression

ODI 12c Expression

ODI 12c Expression

For the filter, join and lookup table nothing changed.

ODI 12c Lockup

ODI 12c Lockup

The set is to define the type of union you can have between the datasets, same as before but now it’s in the mapping too.

ODI 12c Set

ODI 12c Set

We now have a sort component, so now we may stop doing “SQL injection” or “KMs changes” for a simple order by component (of course I liked this one).

ODI 12c Sort

ODI 12c Sort

And the Split component. This one is what I missed the most in OWB. This allow us to say something like: if the DIMENSION is Account, all the data goes to DIM_ACCOUNT, DIMENSION =  ENTITY then DIM_ENTITY, the others goes to DIM_OTHERS for example.

This is a cool thing but easily done using our command on source and target in a procedure (See this post 10 Important Things to Improve ODI Integrations With Hyperion Planning Part 2).

ODI 12c Split

ODI 12c Split

As we can see a lot of things were made for this version, but all this things makes it unusable. Really, in the old versions I already tried to not use interface, only if was absolute necessary, because they are time consuming, inflexible, hard to maintain and there’s nothing you can’t do in a procedure that is a lot better and faster to create then interfaces, in fact I only use interface when I want to use the CKM to use some constraints for data quality, nothing I can’t do in a procedure, but this is for sure something easier to achieve using interfaces. Despite of that, everything else I prefer to use a procedure, mainly because I can get rid of the models, that looks good, but for me they are the true villain of ODI. Models are hard coded information, and I hate hard code.

In resume, in this new version things that were relative simple to use, now are a nightmare to create. Of course that things will get more visual but the developers will pay a very high price for that cool looking. Oracle just added foolish complexity on things that were simple and that worked very well. Do you want a final example? On prior versions of ODI, you would import all KMs that you needed for that specific project and you would pick one of them from the combo box on the flow tab. If you needed to change it later, just pick another one from that same list and that’s it:

KMs 11g

ODI 11g KM Selection

On version 12, first you need to be on the Logical tab of the Mapping object, click on the target table to get its focus, expand “Target” properties on the right panel and select its target “Integration Type”. This type will filter which KMs you will be able to see in the Physical tab:


ODI 12c KM Selection 1

In the Physical tab, click again on the target table, expand “Integration Knowledge Module” and select one of the KMs of that type that you filtered in the previous tab:


ODI 12c KM Selection 2

And what happens if you want to change the KM? If it is from a different type, first you need to go to the Logical tab, change its type, go to Physical, and select another KM. Ok, they have categorized the KMs and this is a good thing but why they didn’t add the Integration Type in the same tab of the KM selector??? Now we need to go back and forth without any good aparent reason and if you are in doubt on which KM to select and you want to read their descriptions to see which one best fits your needs, then you are totally screwed.

But there are two really cool things about this 12c version. First one debugger! Finally they added a debugger to ODI! This feature was a long waited one because it was simply terrible to debug things in ODI. Now you can go execute the code step by step, take a look in the variables content for that session and you can even query uncommitted data through the transactions:


ODI 12c Debug

Second cool stuff: Roles in Security Module! Again, another long awaited simple feature that did not exist until now. Roles are similar to Groups where the security added to a Role is replicated to all users that belongs to that Role. This is great, because in the old days, Security configuration was madness with a lot of manual configuration. Finally now we will have a better Security framework to work on it.


ODI 12c Roles

Well, there’s a lot of thing to see in this new version yet, but the first thing wasn’t pretty. I didn’t uninstall it yet. Let’s see if we can find anything good that justify this living hell that the interfaces (mappings) turned out to be.

If someone of you learn something different or get a different idea for this new version please let us know because I still don’t believe that these changes happened and this is the way Oracle wants us to work from now on. (By the way the UI for procedures are different too and for now I’ll not say if I liked it or not because normally we need some time to get used to it [but I didn’t like it J]).

This weekend we’ll try a migration and let our impressions here.

See you guys!

10 Important Things to Improve ODI Integrations with Hyperion Planning Part 7 (Smart Metadata Loading)

Posted in EPM, Hyperion Planning, ODI Architecture, ODI Architecture with tags , , , , , on September 13, 2013 by RZGiampaoli

Hello everybody. The time arrived to put some intelligence behind our metadata load. After some years working with Hyperion Planning, ODI and DRM (or any other metadata repository), I figure out that 90% of the metadata does not change in the month cycle maintenance (in a normal Planning application). That means, 90% of the time that a metadata integration takes is useless. It’s a lot of time if you are maintaining a big client as one of mine that the maintenance cycle took more than 8 hours for all their regions.

Luckily for them I figure out a very effective and easy way to decrease that time and now it takes less than 30 minutes for the entire maintenance cycle. Basically I developed a method that categorizes each metadata row in our tables, and based in this category the interface knows what it need to do with that data. Let’s see how it works.

After we have an inbound and extract tables with all metadata from source and target systems (as we saw in the part 5 of our series), we need to compare them and decide what to do with each metadata member. For this tie out process we created the metadata tie out table that is a merge of both inbound and extract tables containing all source and target columns with a prefix identifying each one of them plus a column called CONDITION. This extra column is used to describe what the metadata load process should do with that particular member. It is important for this table to have all sources and target columns because then we can actually see what has changed from source to target metadata of that member.

Metadata tie out process will be responsible to read both source and extract tables and populate the metadata tie out table with all source, extract and CONDITION information. The tie out process has a built in intelligence that analyzes several different load situations to each member and populates the final result in the CONDITION column. The tie out process always searches for a parent/member/application/dimension combination in the source table and match it to the parent/member/application/dimension on the target table. The process uses this combination because these are the information that represents a unique member in Planning.

Here are the possible CONDITION statuses created by the tie out process:


When it happens


All metadata information from the inbound source table is equal to the extract table information, so no further action is needed.

No Match

Any column from the inbound source table is not equal to the extract table information. This member will need to be updated in the target Planning Application.

Exists only in Source

If it is a new member and exists only in the inbound source metadata table it needs to be loaded to the Planning Application.

Exists only in the Application

If a member was deleted on the source system but still remains in the planning application. For those cases we created a “Deleted Hierarchy” member and move the deleted members under it. The process doesn’t physically delete the member to keep the data associated with it intact.

Moved Member

If a member moves from one parent to the other and needs to be updated in the Planning Application.

Changed Attribute member

When one attribute is moved from his parents to another parent.

Reorder sibling members

When a new member needs to be inserted in the place where other member previously belongs or a member changed place order with one of its siblings.

Deleted Share Members

When one shared member stops to exist in the inbound table and needs to be deleted from the Planning Application.

The first four conditions status are achieved by a “Full Outer Join” between the Inbound and the Extract table and a “Case When” to define the CONDITION column as we can see in the below:

Tieout Query example

Tieout Query example

This query compares all metadata columns in the source and extracts tables to see what has changed and adds to the CONDITION column what the load process should do with that row afterwards. For the other four conditions status we need to work in the data just created by the figure 9 queries.

  • Moved Members: When we execute the query from Figure 9 we get an unexpected behavior regarding moved members. A moved member is a member that changed from one parent to another. Since the query compares the member and parent names to decide if that is a new, modified or deleted member, it will consider that the source member is a new member (because it has a new parent) and the extracted member will be considered as a deleted member (because its parent/member combination does not exist in the source) generating two rows in the tie out table instead of one. To solve this issue the tie out process merge those two rows into a single one. This merge happens for all multiple rows that have the same member name but one with “Existing only in Source” condition and another one with “Exists only in the Application” condition;
  • Changed Attribute Member: Attribute members require a special logic because Hyperion Planning treats them differently. When you want to move an attribute member from one parent to another, you first need to delete the member and then insert it back in the new parent. So this is a two-step operation, instead of the normal move member operation. When the process deletes the attribute first Hyperion Planning automatically removes its value from its associated dimension member. If we don’t load the associated dimension members again their attribute values will be missing in the end of the metadata load process. To solve this issue the metadata tie out process searches for all dimension members that have a moved attribute associated with it and change their condition to NO_MATCH. This will guarantee that after moving the attribute to a new parent the process also loads all the dimension members again with its attribute values. Another particularity with attributes is that if an attribute doesn’t exist anymore in the source system it is deleted from the planning application. It is not moved to a deleted hierarchy because no data is associated directly with the attribute member, thus no data is lost;
  • Reorder sibling members: When a single member is added to an existing parent member and this parent member has other child members, planning adds the new member in the end of the list. This is because Hyperion planning doesn’t have enough information to know in which order to insert this new member as it does not have its sibling’s orders to compare to it. So the tie out process also search for all existing siblings of the new member and mark them as NO_MATCH to indicate that they should be loaded all together. This way Hyperion Planning will have all siblings orders and will load the members in the correct order;
  •  Deleted Share Members: If a share member ceases to exist in the source metadata, it is removed completely from the planning application. There is no reason to move them to a deleted hierarchy member because no data is associated directly with it;

When the tie out process finishes populating the metadata tie out table we will have all information to load only the necessary members to Planning. As this table is centralized and has all applications and dimensions in it, it is just a matter to loop it for every application and dimension needed to be loaded by the generic load component. To accomplish this,the next post will show how to make the KM and the ODI models dynamic enought to handle this.

See you next time.

10 Important Things to Improve ODI Integrations with Hyperion Planning Part 6 (Metadata validation when loading data to Hyperion Planning)

Posted in EPM, Hyperion Planning, ODI Architecture, ODI Architecture with tags , , , , , on July 25, 2013 by Rodrigo Radtke de Souza

Hi all! It’s good to be back! As we have seen in our last post, we can easily extract all existing metadata information from any number of planning applications, but what can we do with all this information? Well, we can do a lot of great stuff. One of them, as mentioned in the last post, is to compare the existing metadata information to the new metadata that we will load to planning and load just what have changed. This will be covered in details in a later post. Today I’ll be talking about a simpler but very powerful usage of this existing metadata information: metadata validation when loading data to Hyperion Planning!

Everyone that has loaded data into Hyperion Planning using ODI already passed through this situation: you get some data to load into planning and let’s say that this data load takes five minutes to complete. You are happy, the business team is happy, but for some reason in a random day the data load takes six hours to complete. The end user complains, you go check the logs and you find something like that:

2009-07-30 18:00:52,234 DEBUG [DwgCmdExecutionThread]: Error occurred in sending record chunk…Cannot end data load. Analytic Server Error(1003014): Unknown Member [ACT001] in Data Load, [0] Records Completed
2009-07-30 18:00:52,234 DEBUG [DwgCmdExecutionThread]: Sending data record by record to Essbase

This error happens when you are trying to send data using a member that is not part of your outline. ODI is great to load data into Hyperion Planning, but it has a weird behavior when you have an unknown member in you data load. In a perfect world, ODI reads its source database, gets a big chunk of data and sends straight to Essbase. Essbase process it, sends and OK to ODI and ODI sends another big chunk of data. This works pretty fast because it loads big chunks of data at a time, but if you have an unknown member in the data load, Essbase will send to ODI an error stating that there is one unknown member in that data chunk and ODI will switch to “record by record” mode. In this mode ODI will not send a chunk of data but it will send record by record to Essbase and this may take forever depending on how much data you have to load.

I don’t really know why ODI behaves like this, but this is what happens in reality. To avoid that we have a very powerfull technique that we will always talk about: metadata from planning repository. We already know how to read all existing metadata from a Planning application, so it is just a matter to compare all members that we will be sending to Hyperion Planning against all existing metadata in that application prior to the load. This will guarantee that only existing members of that application will be sent to Essbase, insuring that ODI will not flip to “record by record” mode.

How can we accomplish that? We have a lot of possibilities as it depends on the current database structure and tables that your system has, but I will display one that is generic enough to be applicable to any system.

1) Create a table with the following structure:


You may want to add more columns to this table if you need, but those should be fine for our example. This table is used to store all existing metadata from any number of Hyperion Planning applications that you may have. You may populate it using the techniques seen in our last post.

2) Create an inbound table containing the data that you will send to Hyperion Planning. This table will contain one column for each dimension that you may have plus a column named “DATA” that will contain the value of that intersection and APP_NAME that will contain the name of the Hyperion Planning application which that data will be loaded to. Here is one example:


Why do we need to create this table? As I said, there are many ways to do this verification and maybe you may not need to create it, but I strongly recommend doing so. The reason is that you create a data layer between your source system and Hyperion Planning that is centralized in one single point where you can have data for all applications that you may have (you may partition this table by app for example) and you may add centralized ODI check constraints in one single table as we can see below.

3)  Create ODI check constraints to validate all dimension members. For each dimension column in your INBOUND_GENERIC table, you will create an ODI check constraint that will validate those members against the existing metadata in that application. Let’s use ACCOUNT as an example:


Go to INBOUND_GENERIC model in ODI and add a New Reference constraint. Change the type to “Complex user reference” and select your model that contains the TBL_EXISTING_METADATA table.


Go to Expression tab and add your constraint SQL there as below:


Here we are comparing all members in ACCOUNT column in our INBOUND_GENERIC table against all ACCOUNT members in TBL_EXISTING_METADATA table that has a specific PLAN_TYPE and DATASTORAGE. Again, this is just an example and you may tweak it to your reality. You will do this to all dimensions that you may have and you may also add other constraints as duplicated keys, invalid amounts and so on:


4) The last part is just a matter to select a CKM in your ODI interface that will load the INBOUND_GENERIC table and see the results. You will have INBOUND_GENERIC table with only metadata that exists in your Hyperion Planning application and an E$ table (created by ODI CKM) with all non-existing members in your outline!


Now you may load from INBOUND_GENERIC table to Hyperion Planning application with the guarantee that it will always run fast without worrying about unknown members in your outline. Also as a bonus, you have E$ table with all members that are missing in the outline, so you may use it to warn the end users/support team and so on.

I hope you all enjoy!