Oracle Business Intelligence Application 7.9.6.3 Customization Types, Tips and Best Practice Guidelines

I would like to share some of the Oracle Business Intelligence Application (OBIA) customization tips and best practices guidelines from my knowledge base. This document is based on my experiences of BI Apps implementation over the period of last 10/11 years since Siebel days and extract from Oracle’s standard documentation around BI Apps.

OBIA Architecture

OBIA Architecture has following components i.e. ETL Architecture, Data Model, Metadata (Repository), Dashboard/Webcat along with security.

OBIA Pre-Built Contents

Oracle Business Intelligence Applications 7.9.6.3 are pre-built solutions from source to dashboard. The pre-built solution contents are:   

Pre-built Analytics Data Warehouse Model

The Business Analytics Data Warehouse is the persistent layer where information is stored for retrieval when required. The data warehouse resides in a relational database platform like Oracle, IBM DB2 LUW, Teradata or Microsoft SQL Server. It consists of transaction grain tables used to capture facts from the key business processes across the organization. The model consists of current snapshots, periodic snapshots and aggregate tables to support complex analysis requirements along with the hundreds of dimension tables conformed across the fact tables which contribute to enrich the analyses.

Pre-built Data Integration Components (ETL/E-LT)  

Data Integration components ( data extraction routines) are used to integrate data from various operational systems such as E-Business Suite, PeopleSoft Enterprise, Oracle's Siebel CRM Applications, Oracle JD Edwards Applications, and third party legacy systems, and load the information in the Business Analytics Data Warehouse.

The data integration components are built using either the Informatica Power Center data integration platform (ETL) or Oracle Data Integrator (E-LT). It uses a layered approach based on data warehouse best practices, which is open and flexible which provides ease of configuration and customization to enable changing the out of the box transformation rules, adding additional attributes, facts and dimensions.

Pre-built Metadata (BI Repository)

The metadata layer includes the definition of role based views, dimensions, metrics, hierarchies, calculations, aggregation rules, canonical dime dimensions, time series metrics, roles and security. The metadata layer is built on the Oracle Business Intelligence Enterprise Edition platform using the Admin Tool, a core component of the BI server, which is designed to meet the requirements of the Common Information Model to deliver consistent and integrated information to business users, by capturing the data from multiple source systems. Oracle Business Intelligence Applications provides ready to use pre-built metadata covering dimensions, metrics and hierarchies. In addition, the pre-built metadata includes the capability to leverage the security profiles inherited from operational systems such as Oracle EBS, PeopleSoft and JD Edwards.

Pre-built OBI-EE dashboard and report content (Catalog)

The OBIEE Presentation Catalog contains pre-built metrics, analytic decision workflows, reports, dashboards and alerts used to deliver information, stored in the Business Analytics Data Warehouse, to information consumers. These services are built on the Oracle Business Intelligence Enterprise Edition delivery platform, which is designed to meet the requirements of an enterprise business intelligence solution. Pre-built reports and dashboards are built on industry best practices, and provide rich content covering functional areas including sales, service, marketing, finance, supply chain, order management, procurement and human resources.

Customization Best Practice – Keep it to Minimum

In line with other Oracle applications products in general, customization should be kept to minimum and wherever possible out-of-the-box (OOTB) functionality should be employed.  This should be done in such manner that future upgrades to the product will not require significant amount of rework, i.e. where there is a good match between the requirements and the out-of-the-box functionality. However, in reality it is often unavoidable. 

Customization Reality – It is Unavoidable

It is often seen that certain key business requirements are not covered by the pre-built contents (Out of Box contents), For example the OOTB Data Warehouse may not include certain attributes customer would like to report on from the source system, or more likely, require an external secondary data store (file, RDBMS, XML, and OLAP) to include additional reporting matrices.

For a rapid development we must re-use as much of the OOTB data model as possible to avoid “re-inventing the wheel” by developing data objects that have already been populated by BI-Apps. There are number of situations where there is a need to modify the pre-built contents.

In these cases it is important to follow certain guidelines and techniques when developing non-standard BI elements. This blog is an attempt to detail guidelines on how to develop custom objects to integrate into BI Apps. The idea is to follow the same design techniques and guidelines that BI Apps uses to build OOTB contents for customization. This approach would be helpful to maintain the oracle’s high standard development practices and keep “look and feel” like standard BI-Apps content.

Types of Customization and Impacts

There are 6 types of customizations that will be implemented.  These customizations can be as simple as exposing already existing attributes in the DW to the Presentation Layer of OBI-EE.  They can also be as complex as creating new dimension, facts and external data sources. The new report creation is not in scope of the this discussion.

The following are the main extensions covered in this blog.

TYPE A- Simple Extensions and Adding Simple/Complex Logical Definition

The Data Warehouse has a vast amount of attributes within its data model.  However, not all this data is exposed to the repository and dashboards.  One of the simplest customizations is to expose the attribute top the Presentation Layer of OBI-EE.

Creating new logical definitions (simple-moderate-complex) based on physical/logical columns already exposed should also be included into the Type A category. Time Series Logical columns are an example of this type of customization.  

Impact:

·     Changes only in the OBI-EE repository in the Physical, Logical and Presentation Layer are needed.

TYPE B- Attribute Extensions

Adding attributes to existing dimensions and measures to existing facts.  The complexity of this customization depends on the SQL required to populate the attribute.  If the attribute is part of an existing table in an existing query then the inclusion is very simple.

Impact: 

• Additional column(s) to the physical staging and target tables.
• Changing existing SDE source mapping to add the additional column(s) to the SQL query and the target table.
• Changing existing SIL target mapping to add the additional column(s) to the SQL extraction query to the target table.
• Changes required to the OBI-EE repository to add the column to the Physical and modeled through to the Business and Presentation Layers.

TYPE C- Additional Fact, Parent Child Hierarchies Table, Bridge Table

Adding new Facts, Parent Child Hierarchies (value hierarchies) or Bridge tables (a technique for modeling Many-to-Many relationships) will require new SDE mappings for extraction to the DW staging area and new SIL loading routines from the staging area to the DW 

Impact:

• Additional physical tables created in the staging and DW areas.
• Add new SDE source mapping to populate the target staging table
• Add new SIL target mapping to populate the DW target table
• Import the new data objects to the OBI-EE repository and model the physical through to the Business and Presentation Layers.
• Optionally applying security filtering.
• Adding new SDE and SIL to the DAC dependency

TYPE D- Localization of Oracle Business Intelligence (Repository Metadata, Oracle BI Data or Report and Dashboard)

Localization of Oracle Business Intelligence requires three configurations i.e. localization of Oracle BI Repository Metadata, localization of Oracle BI Data and localization of Reports and Dashboards. These configurations require configuration file changes, creating metadata objects to support it which includes initialization block, externalize metadata objects etc , DW tables and routine to populate the language dependent data.

Impact:

• Additional translation tables/ Modification to existing columns in staging and DW areas.
• Add/Modify SDE source mapping to populate the target staging table.
• Add/Modify SIL target mapping to populate the DW target table.
• Session Variables and Configuration Settings and creation of supporting metadata objects
• Import the data objects to the OBI-EE repository and model the physical through to the Business and Presentation Layers.
• Adding new SDE and SIL to the DAC dependency if required  

TYPE E- Fact Granularity Change

Add new Dimensions to an existing Fact requires changes to existing SDE and SIL mappings for the Fact table.  In addition, SDE and SIL mappings are required to implement new dimensions.

Impact:

• Additional physical dimension table created in the staging & DW areas.
• Additional key fields to staging and DW fact tables.
• Add new SDE source mapping to populate the new target staging table.
• Add new SIL target mapping to populate the DW target table.
• Changing existing SDE source mapping to add the additional keys staging fact table.
• Changing existing SIL target mapping to add the additional key column(s) to the DW target fact table.  Also, include the key lookup to the new dimension.
• Create SDE & SIL workflows.
• Create DAC tasks with required dependencies.
• Import the new data objects to the OBI-EE repository and model the physical through to the Business and Presentation Layers.
• Changes required to the OBI-EE repository to model new Physical model and modeled through to the Business and Presentation Layers.
• Optionally applying security filtering.

TYPE F- New Star Schema

New facts and some new dimensions integrated with existing dimensions.

Impact:

• Additional physical dimension and fact table(s) created in the staging & DW areas.
• For each new physical dimension table and fact table a new SDE source mapping to populate the new target staging tables.
• For each new physical dimension table and fact table a new SIL target mapping to populate the DW target table.
• Create SDE & SIL workflows
• Create DAC tasks with required dependencies.
• Import the new data objects to the OBI-EE repository and model the physical through to the Business and Presentation Layers.
• Optionally applying security filtering.

TYPE G- Additional Source Systems or Using Universal Adaptors

Adding a second home-grown or non-supported data source.  OOTB BI-Apps has one supported data source.  However, it is possible that you want to integrate other data sources into the Business Analytic Warehouse (BAW).  Additional sources can include simple spreadsheets to interlacing data into existing data models. 

Interlacing is a method of loading data into the existing data model without matching & merging into the supported data source.  For example, there might be Purchase Orders from a different ERP system that you want to provide integrated reporting with eBS.  In this case you will need to integrate dimensional and fact data.

Another example is to use the universal adaptors to push the data into the BI Apps. The universal adaptors implementation lists following impacts.

Impact:

• Additional columns created for additional attributes in dimension and fact table(s) created in the staging & DW areas.
• Modify the universal adaptor SDE mapping &  SIL mappings to include additional attributes if any
• Create SDE workflow
• Create DAC tasks with required dependencies.
• Import the new columns data objects to the OBI-EE repository and model the physical through to the Business and Presentation Layers.

In the next blog of this series I would like to cover the naming conventions and development guidelines that should be followed when developing new attributes, tables, dimensions and facts in the Business Analytics Warehouse (BAW) and new/changed repository objects in the OBIEE repository.

I would like to cover the naming conventions and development guidelines that should be followed when developing new attributes, tables, dimensions and facts in the Business Analytics Warehouse (BAW) and new/changed repository objects in the OBIEE repository.

In this blog my focus would be more on database objects modification standard practices and guidelines such as tables, columns and indexes.

Why Best Practices? – The standard and guidelines for database schema, tables, columns and indexes is for better manageability and simplify the future upgrades. In long term it would help to reduce the application day to day cost and successive upgrades.

  

Database Schema and Objects

The Business Analytics Warehouse (BAW) is the pre-built Data Warehouse as part of BI-Apps.  The database schema will be decided by the installation team.  A default is “BAW”.  It contains both the staging tables and DW target tables. All customized tables, columns, indexes and other database objects MUST be added to BAW schema. (Best Practice DB 1)

Why to add in BAW Schema? – For better manageability and avoid any cross schema joins at OBIEE or Informatica Level as I understand cross schema joins within repository and Informatica would more complicated than keeping it simple. The mantra is keep it simple.

The database objects changes which is been done within the database schema and objects should use best practices for creating and managing versions and comments option of underline databases. The database objects customization should use versions and comments features of database for better traceability. (Best Practice DB 2)

Why Versions and Comments? Putting comments on tables and columns based on phase of projects, user created and date would be useful and provide enough traceability of customization done.

In addition make sure that what so ever customization done to database objects should be imported into the DAC container. The database objects customization should be imported into the DAC container. (Best Practice DB 3)

Why Import? DAC should be your central repository which would be used to migrate the database objects customization from development environment to test and then production. We should avoid using word/text file as a storage option for database objects.

Tables

Naming of Tables

The naming conventions used for tables in the Oracle BI Data Warehouse are as follows.  The naming conventions for tables defined in BI Data Warehouse should be followed if custom tables are created in the warehouse. (Best Practice DB 4)

Why Standard Naming Conentions? It would help to identify meaning of each of the table and its significance and usability in overall solution.

The names of the Oracle Data Warehouse tables consist of three parts:

Prefix

Name

Suffix

The three components are capitalized and separated by an underscore (_), e.g. W_GL_OTHER_F. Each component has a meaning, as described in the table below:

Component	Value	Meaning
Prefix	W_	Out-of-the-box warehouse table
Prefix	WC_	Custom warehouse table
Suffix	_F	Fact table (base)
Suffix	_FS	Staging Fact table (base)
Suffix	_A	Aggregate fact table (base)
Suffix	_D	Dimension table (base)
Suffix	_DS	Staging Dimension table (base)
Suffix	_DH	Dimension hierarchy table (base)
Suffix	_SCD	Slowly changing dimension table (base)
Suffix	_MD	Mini-dimension table
Suffix	_H	Helper table
Suffix	_HS	Helper Staging Table
Suffix	_TMP	Pre Staging Temporary table
Suffix	_G, _GS, _S	Internal Table
Prefix	_UD	Unbounded dimension
Prefix	_DHLS	Staging for Dimension Helper
Prefix	_DHL	Dimension Helper Table
Prefix	_M	Map Dimension

Table Types

The types of tables used by the Oracle Business Analytics Warehouse are as follows. The developer should use following types of tables; I believe the list of the tables used has covered almost all types of tables possible for DW solution. Any new addition would be a treated as a new entrant in the family and should be welcomed. (Best Practice DB 5)

Why different types? The type of the table and its description would be helpful to understand its significance in overall solution

Table Type	Description
Aggregate tables (_A)	Contain summed (aggregated) data
Dimension tables (_D)	Star analysis dimensions
Staging tables for Dimension (_DS)	Tables used to hold dimension information that have not been through the final ETL transformations.
Staging tables for Usage Accelerator (WS_)	Tables containing the necessary columns for the ETL transformations
Dimension Hierarchy tables (_DH)	Tables that store the dimension's hierarchical structure
Dimension Helper tables (_DHL)	Tables that store M:M relationships between twojoining dimension tables
Staging tables for Dimension Helper (_DHLS)	Staging tables for storing M:M relationships between two joining dimension tables
Fact tables (_F)	Contain the metrics being analyzed by dimensions.
Fact Staging tables (_FS)	Staging tables used to hold the metrics being analyzed by dimensions that have not been through the final ETL transformations
Internal tables (_G, _GS, _S)	Internal tables are used primarily by ETL mappingsfor data transformation and controlling ETL runs
Helper tables (_H)	Helper tables are inserted between the fact and dimension tables to support a many-to-many (M:M) relationship between fact and dimension records
Map dimension tables (_M)	Tables that store cross-referencing mappings between the external data ID and the internal ID
Mini dimension tables (_MD)	Include combinations of the most queried attributes of their parent dimensions. The database joins these small tables to the fact tables
Primary extract tables (_PE)	Tables that are used to support the soft delete feature. The table includes all the primary key columns (integration ID column) from the source system. When a delete event happens, the full extract from the source compares the data previously extracted in the primary extract table to determine if a physical deletion was done in the transactional database. The soft delete feature is disabled by default. Therefore, the primary extract tables are not populated until you enable the soft delete feature
Unbounded dimension tables(_UD)	Tables containing information that is not bounded in transactional database data but should be treated as bounded data in the Oracle Business Analytics Warehouse

Columns

Naming of Columns

This section describes the naming conventions used for columns in the Oracle BI Data Warehouse.  The naming conventions for columns defined in BI Data Warehouse should be followed if custom columns are created in the warehouse. (Best Practice DB 6)

Why Naming Convention for Columns? It would help to identify meaning of each of the column and its significance and usability in overall table usage.

The columns of the Oracle Data Warehouse tables consist of three parts:

Prefix

Name

Suffix

The three components are capitalized and separated by an underscore (_), e.g. X_BUDGET_AMT. Each component has a meaning, as described in the table below:

Component	Value	Meaning
Prefix	X_	Custom warehouse column
Suffix	_AMT	This column contains a currency amount
Suffix	_CD	The column value is based on the contents of the List of Values (LOV)
Suffix	_DT	This column contains a date or date time value.
Suffix	_FLG	This column contains a Boolean value where Y indicates Yes or True; N indicates No or False
Suffix	_I	Language Independent Code
Suffix	_ID	Columns are used in staging tables, they corresponds to the source identifier
Suffix	_WID	Identifier generated by Oracle BI linking dimensionand fact tables, except for ROW_WID
Suffix	_NAME	Name corresponding to the code column (columns ending with _CODE)
Suffix	_DESC	Long Description corresponding to the code column (columns ending with _CODE)
Suffix	_NUM	This column contains a number or an identifying alphanumeric value

All new tables, whether Fact or Dimension MUST contain the following important fields :( Best Practice DB 7)

Why System Fields? It would provide metadata information about the data and DML operations. Metadata of data such as source identifier, source type, created by, creation date etc is useful in many ways such as setting up changed data capture, setting up DML operations such as insert, update and up-sert.

Column Name	Column Type	Required in Area	Description
INTEGRATION_ID	VARCHAR2(80 CHAR)	Staging / DW	Stores the primary key or unique identifier of a record in the source table.
DATASOURCE_NUM	NUMBER(10)	Staging / DW	Stores the data source from which the data is extracted – this is setup in DAC.  E-Business Suite = 4.  New external sources should have their own unique number.
ETL_PROC_WID	NUMBER(10)	DW	Stores the ID of the ETL process information; details of ETL process are stored in the W_ETL_RUN in the warehouse
ROW_WID	NUMBER	DW	Is the sequence number generated during the ETL process; unique identifier (sequence number) for the tables.
CREATED_ON_DT	DATE	Staging / DW	Stores the creation date of a record in the primary source table
CHANGED_ON_DT AUX1_CHANGED_ON_DT AUX2_CHANGED_ON_DT AUX3_CHANGED_ON_DT AUX4_CHANGED_ON_DT	DATE	Staging / DW	Stores the last update date of a record in the primary source table Additional column prefixed by AUX# can be added to store the last update date related to auxiliary source tables
CREATED_BY_ID	VARCHAR2(80)	Staging	Stores the user id who created a record in the primary source table
CREATED_BY_WID	NUMBER(10,0)	DW	Surrogate key of the user id who created a record in the primary source table
CHANGED_BY_ID	VARCHAR2(80)	Staging	Stores the user id who last updated a record in the primary source table
CHANGED_BY_WID	NUMBER(10,0)	DW	Surrogate key of the user id who last updated a record in the primary source table
TENANT_ID	VARCHAR2(80)	Staging / DW	This permits to distinguish distinct source instances having a common data source num identifier Default value is ‘DEFAULT’

Miscellaneous Technical columns for new tables

All new tables, MAY contain the following important fields:( Best Practice DB 8)

Why Flg Columns ? It would provide metadata information about the data and DML operations. Metadata of data such as source identifier, source type, created by, creation date etc is useful in many ways such as setting up changed data capture, setting up DML operations such as insert, update and upsert.     

Column Name	Column type	Required in Area	Description
CURRENT_FLG	CHAR(1)	DW	For Slowly changing dimension purpose
DELETE_FLG	CHAR(1)	Staging / DW	For Soft/Hard delete purposes
W_INSERT_DT	DATE	DW	optional insertion date for ETL audit purpose
W_UPDATE_DT	DATE	DW	optional update date for ETL audit purpose
SRC_EFF_FROM_DATE	DATE	Staging / DW	For Type 2,3 or 6 slowly changing dimension purpose
SRC_EFF_TO_DATE	DATE	Staging / DW	For Type 2,3 or 6 slowly changing dimension purpose
EFFECTIVE_FROM_DATE	DATE	Staging / DW	For Type 2,3 or 6 slowly changing dimension purpose
EFFECTIVE_TO_DATE	DATE	Staging / DW	For Type 2,3 or 6 slowly changing dimension purpose

Miscellaneous Technical columns for new tables

The currency codes for related system columns are as follows:( Best Practice DB 9)

Why Currency Codes? To support reporting in multiple currencies is one of the prime features of BI Apps implementations.       

System Column	Description
DOC_CURR_CODE	Code for the currency in which the document was created in the source system.
LOC_CURR_CODE	Usually the reporting currency code for the financial company in which the document was created.
GRP_CURR_CODE	The primary group reporting currency code for the group of companies or organizations in which the document was created.
LOC_EXCHANGE_RATE	Currency conversion rate from the document currency code to the local currency code.
GLOBAL1_EXCHANGE_RATE	Currency conversion rate from the document currency code to the primary group currency code.
GLOBAL2_EXCHANGE_RATE	Currency conversion rate from the document currency code to the GLOBAL1 currency code.
GLOBAL3_EXCHANGE_RATE	Currency conversion rate from document currency code to the GLOBAL2 currency code.

Indexes

Naming of Indexes

The naming conventions used for Indexes in the Oracle BI Data Warehouse are as follows. The naming conventions for indexes defined in BI Data Warehouse should be followed if custom indexes are created in the warehouse. (Best Practice DB 10)

Why Naming Conventions for Indexes ? It would help to identify custom indexes and its significance and usability in overall solution.

The index name of the Oracle Data Warehouse tables consists of two parts

Prefix

Name

Suffix

The three components are capitalized and separated by an underscore (_), e.g. W_GL_BALANCE_F_U1, each component has a meaning as described in the table below.

Component	Value	Description
Prefix	W_	Standard Data Warehouse Table
Prefix	WC_	Custom Table
Name	XXX	Table Name (e.g. W_GL_BALANCE_F)
Suffix	_U1	Unique Index ( e.g. composite index on INTEGRATION_ID and DATASOURCE_NUM_ID
Suffix	_F1	Non Unique Dimensional Reference Key Index ( e.g.  bitmap index such as BALANCE_DT_WID )
Suffix	_M1	Non Unique Index ( e.g. bitmap or normal index or composite normal index )

Best Practices for Indexes

The best practices for the indexes are as below.

·         The custom indexes in Source System ( e.g. Oracle EBS) for incremental Loads performance can be categorized into following groups and action need to taken based on facts

o   Tables that do not have indexes on LAST_UPDATE_DATE in the latest EBS releases, but there are no performance implications reported with indexes on LAST_UPDATE_DATE column.

o   Tables that have indexes on LAST_UPDATE_DATE columns, introduced in Oracle EBS Release 12.

o   Tables that cannot have indexes on LAST_UPDATE_DATE because of serious performance degradations in the source EBS environments.

The indexes on source system should have prefix OBIEE_ and DO NOT follow to the standard source system index naming conventions (e.g. OBIEE_OE_ORDER_HEADERS_ALL)(Best Practice DB 11)

Why? This is to make sure that these custom indexes are managed separately from the rest of the standard indexes.

·         Staging tables typically do not require indices, not a strict rule

Don’t hesitate to include indexes on staging table if necessary(Best Practice DB12).  

Why? Let common sense prevails, I hate strict rules; if you prove the index would help you in data load performance don’t worry go ahead and just do it.

·         Create indices on all columns that the ETL uses for dimensions and facts. For example:

o   ROW_WIDs of dimensions and facts

o   INTEGRATION_ID

o   DATASOURCE_NUM_ID

o   Flags

o   Dimensional Reference Keys  of fact tables

Create indices on all system columns such as ROW_WID, INTEGRATION_ID etc used within ETL processes.This is absolutely MUST for ETL performance. (Best Practice DB13).    

Why? ETL Performance, without these indices I can tell you that your ETL won’t run efficiently.

·         Create Bitmap Indexes for Dimensional reference keys and flags, treat this a MUST requirement. (Best Practice DB14).   

Why? With Bitmap Indexes for Dimensional reference keys and flags allows us to use STAR TRANSFORMATION.I believe it is absolutely essential to achieve best query performances.

Make sure that the database parameter for STAR TRANSFORMATION & Repository Database Parameters setting has been in place.  

·         Carefully consider on which columns to put filter conditions (Best Practice DB15).

Why? Setting up index on column which we used to put condition in report would improve performance for obvious reason.

·         Define indices to improve query performance(Best Practice DB16)

o   Inspect standard repository objects for guidance

Why? The index creation processes should be driven by business requirements rather than physical structures and conventional wisdom. I believe the reporting system is a mainly a business support system and should be driven by business requirements.

·         Register new indices in the DAC, treat this as a MUST requirement ( Best Practice DB17)

Options within DAC are

o   Local or Partitioned Index

o   Query Index or ETL Index

o   The DAC server drops and re-creates indices during a full load and /or incremental load

Why?  As I said earlier DAC should be your central repository for all database objects which includes indexes. This is helpful during migration of code from development instance to test and finally to production. DAC would be useful to maintain drop and re-recreate indices during full load and/or incremental load.

·         Review/Revisit Index Strategy on regular basis ( Best Practice DB18)

Why?  Review and Revisit the Indices should be done on regular basis, particularly with every new release which might end up with different usage pattern and query performances.

·         The best practices for handling BI Applications indexes in Exadata Warehouse (Best Practice DB19):

Why? Exadata, Exalytics are specially designed engineering system and requires different approach. Based on my experience in this area the success mantra is don’t rush to any conclusion, make sure that proper comprehensive benchmarking is done before dropping or disabling any indices.

o   Turn on Index usage monitoring to identify any unused indexes and drop / disable them in your environment.

o   Consider pinning the critical target tables in smart flash cache

o   Consider building custom aggregates to pre-aggregate more data and simplify queries performance.

o   Drop selected query indexes and disable them in DAC to use Exadata Storage Indexes / Full Table Scans only after running comprehensive benchmarks and ensuring no impact on any other queries performance.

I hope you find the second part of this series informative and useful. My plan is to write next couple of blogs on guideline on Informatica, DAC and Repository customisation.