Successful BI Apps Implementation Part 2: BI Apps 7.9.6

January 23rd, 2014 by

Welcome to Part 2. If you missed Part 1, where I give an introduction to BI Apps and discuss the project life cycle, the link is below.

Successful BI Apps Implementation Part 1: Introduction and the Project Life Cycle

Successful BI Apps Implementation Part 2: BI Apps 7.9.6

In this post, I get technical and take a deeper look at BI Apps 7.9.6. Here goes!

Customising the Data Model

As defined by Oracle, customisations fall into the following categories:

  • Category 1 customisations where we add a new column to an existing fact or dimension table.
  • Category 2 customisations where we create new fact or dimension tables.
  • Category 3 customisations where we add an additional data source to an existing fact or dimension table.
  • Other trivial customisations such as changing filter conditions.

Typically, with the trivial and category 1 customisations, there isn’t much to go wrong. We may need to think about auxiliary change capture (see below) if the column comes from a table other than the base table in the OLTP.

With the category 2 customisations, we need to consider whether new fact tables are suitable for being added to an existing subject area or (more commonly) whether they merit a subject area of their own. I sometimes see subject areas with multiple fact tables and many non-conformed dimensions. This happens when a subject area is iteratively expanded again and again until the usability of the subject area is severely diminished. Things have probably gone wrong if either a) it is possible to create queries that the BI server refuses to interpret or b) the development process constantly involves setting metrics to total over non-conformed dimensions.

Regarding category 2 customisations involving new dimensions, another complication that arises is the modelling of many-to-many (M:M) relationships from the OLTP. Take this example: a user looks at an opportunity in Siebel and as well as seeing all the fields that are visible in OBIEE, they also see a field called Sales Team. The user asks for this to be added to the Sales – CRM Pipeline subject area. This is non-trivial to model in OBIEE as the Siebel Sales Team field actually opens a shuttle applet that displays the multiple sales team members for the opportunity. It might seem obvious that Sales Team can’t be modelled as an attribute of the opportunity dimension but what should be done instead? 99% of the time, my advice would be to examine the business reason behind the request to add the column and find an alternative. It may be that just the primary sales team member is required for reporting purposes or that the Created By or Last Updated By users could be used instead. In the remaining 1% of cases we can consider the use of bridge tables, but considering the number of OOTB metrics that would have to be remodelled to accommodate this change, the functional advantage gained from the change may not be enough to justify such dramatic technical changes. In situations like this, knowing when not to customise can be important for both ease of use and ease of upgrade.

Category 3 customisations do not strictly require RPD changes but we should at least have a column to tell us which source system the row of data originated from. Also, if factual rows from any particular source system cannot be reconciled with certain dimensions (due to the lack of any foreign key column), it can be beneficial to add a row to that dimension (with ROW_WID equal to -1 for example) to cater for these fact rows. It allows us to differentiate between rows from an Oracle source system that do not have an associated dimension row and rows from a non-Oracle source system that cannot have an associated dimension row.

Data Lineage / Tracking Customisations

One of the questions that I sometimes get asked by end users is ‘where does this column come from in EBS / Siebel?’. When dealing with OOTB BI Apps, this is usually quite easy to answer because:

  • Even though our semantic model may be extremely vast, it is usually quite simple and
  • Oracle provides good data lineage documentation and more importantly, descriptive text in the RPD which manifests as hover-over text in the UI.

However, I often find that after a couple of years of customising, the question isn’t so easy to answer. From an IT point-of-view, it’s usually quite easy to determine what is vanilla and what is custom, but from an end-user point-of-view, the exact functional definition of custom columns is not always that obvious. The simple solution is to follow the same standards that Oracle adheres to for the vanilla material: give new subject areas, presentation tables and presentation columns useful descriptions; keep subject areas small and intuitive (1 or 2 fact tables per area); and maintain a data lineage spread sheet that maps columns between source applications and the Oracle data warehouse.

This documentation becomes very useful when we come to upgrade BI Apps. Typically, BI Apps upgrades involve a combination of automated and manual code merging – using a 3-way repository merge on the RPD and some more manual steps to re-apply customisations to the new vanilla Informatica metadata. When testing the merged RPD and customising copies of the new Informatica mappings, the new code should be functionally identical but may be technically different due to data model changes between BI Apps versions. At this point, the above documentation becomes invaluable.

Indexing and Partitioning

In my previous post, I talked about building performance testing and monitoring into the project lifecycle. Here we will focus more on the design and development stages.

In the above section, I describe category 1 customisations as extremely simple. Well, they are, but the one mistake that people often make is not considering whether an index should be created along with the new column. This can lead to a slow but steady increase in average query times as more and more non-indexed columns are used to group and filter data. Generally, if any dimensional attribute is going to be used for grouping or filtering, it will probably benefit from an index. The decision about whether to make it a bitmap index depends on data density. For example, for the field ‘Customer Priority’ (high/med/low), use a bitmap and for ‘Customer ID’, don’t. Also, if a new column is used as a key in an update statement, make sure that the corresponding index has a type of ‘ETL’ in DAC so that it is not dropped as part of the ETL.

Partitioning is an interesting topic in the context of BI Apps. Typically, OOTB BI Apps reports and dashboards perform well even with large data volumes, due to the thousands of indexes that are provided with the OBAW. Therefore, why partition? I can think of 2 main reasons:

  • We have extremely large data volumes in some of our fact tables and have decided to partition by year and include a corresponding filter (i.e. current year) in one of our logical table sources.
  • We have performed a category 3 customization or similar and again have a logical table source filter that could be used as a partition key. For example, our revenue fact contains rows sourced from both Siebel and another system, and only the Siebel data should be visible in the OOTB subject areas.

In both of the above scenarios, we know that the partition key will be used in queries generated by the BI server due to the RPD customisations that accompany the partitioning. So what about scenarios that don’t involve the corresponding RPD changes? Should we consider partitioning to speed up a certain group of reports that have certain filters applied? In my opinion, no, we should only consider it as a viable option when we can guarantee that a) the partition key will be used in queries and b) we expect little or no movement of rows across partitions. Even if these criteria are met, we should only be looking at partitioning after we are convinced that our indexing strategy is perfect. Why? Because badly chosen partition keys can make performance worse! Even the addition of a partition that speeds up some reports can have a negative impact on those that do not reference the partition key due to the increased number of I/O operations involved in reading indexes on multiple partitions.

One important point to note is that bitmap indexes must be defined as LOCAL on partitioned tables. This means that we have to change how DAC creates its OOTB bitmap indexes if we partition an OOTB table. This can be done using Index Actions in DAC but should serve as another deterrent to unnecessary partitioning!

Customising the ETL

There are two main types of customisations that we make to the OOTB DAC and Informatica metadata. Firstly, we may take a copy of an OOTB mapping and make some minor changes. Typically, these will include new columns, sources and lookups, depending on the type of mapping. Secondly, we can create some custom mappings to extract from new sources or load into new targets. Before I give any advice about making these changes, let me first make a point about ETL vs ELT.

In OBIA 7.x, the Informatica server is the ETL engine. Compare this with Oracle Data Integrator where we typically use the Oracle Database as the ELT engine. With ODI, a fact mapping can be used to generate a single SQL statement that takes data from a staging table and loads into a target, populating the necessary foreign keys and generating surrogate keys where necessary. With Informatica, this will instead involve multiple SQL statements just to extract the staging data and data used in lookups, followed by a large amount of processing on the Informatica server itself. Finally, row-by-row insert / update statements will be issued to the target table. Clearly, this is less efficient than the ELT option regardless of how much optimisation we do on the Informatica server side.

The above weakness of the tool means that when we need a mapping to exclusively perform updates, it is often tempting to add some custom SQL to the end of a mapping / workflow rather than creating a whole new mapping that will a) take longer to develop and b) probably run slower. The problem with this approach is code readability. The same applies for the use of stored procedures within mappings (which is often not great for performance either). So my advice is to minimise the number of PLP mappings where possible (save them for populating aggregate tables) and to stick to standard Informatica development methods where possible.

Changed Data Capture

Changed data capture (CDC) is one of the topics that seem to create problems for BI Apps developers. It’s not an intrinsically difficult concept to master but I have seen it overlooked in both the design and testing phases of a couple of projects (mainly when working with Siebel). It’s worth pointing out that CDC is handled very differently for different source systems due to the varying degrees of metadata capture and audit trail functionality between systems.

CDC for EBS is pretty simple – EBS tables have nice metadata columns such as LAST_UPDATE_DATE that allow us to easily extract data that has changes since the last ETL run (minus a few days). Siebel is slightly more complicated and involves creating change capture tables and triggers in the OLTP database.  We still have nice metadata columns like in EBS, but we have to worry more often about auxiliary change capture (see below) and tracking hard-deletes. When working with Peoplesoft, complications exist because update timestamps do not exist on some base tables.

Importantly, when customising the Oracle Business Analytics Warehouse and adding new source tables to the ETL, we must consider the CDC requirements for each new source. As an example, imagine we are loading opportunities from the S_OPTY table in Siebel. By default, our SDE mapping will extract records from the associated change capture view V_OPTY that has been left outer joined to a bunch of auxiliary tables. Now imagine that we need to extract a new custom column called X_IMPORTANCE from the auxiliary table S_OPTY_X. The steps to achieve this are obvious – add S_OPTY_X as a source in the SDE mapping and create a left outer join between S_OPTY and S_OPTY_X, then map the new column from source to target. However, what happens when the value of X_IMPORTANCE is updated in the OLTP? Do we see the update in the OBAW? The answer is ‘maybe’, it depends if the core opportunity record also got updated since the last ETL.

By default, the change in the auxiliary table will not lead to the corresponding opportunity appearing in our change capture view. If we want to see the update in OBIEE without relying on an update to the base opportunity record, we must create or extend a separate mapping that exists purely to populate the change capture view used in the SDE mapping. In this situation, there is a trade-off between functionality (seeing all source system updates in OBIEE) and ETL performance (due to the extra auxiliary change capture process) but I advise starting with the assumption that auxiliary change capture is required. I have seen faulty auxiliary change capture go unnoticed a couple of times so make sure that this functionality is tested!

That’s it for BI Apps 7.9.6. Keep an eye out for more posts in 2014 where I will be blogging about BI Apps 11g.

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