OBIEE security LDAP realm provider JAAS Control Flag options

LDAP Configuration Control Flag Option

When you create a new LDAP, Active directory(ADSI) provider to realm in Weblogic Console, you will be able to see an option called 'Control Flag' with the below four set of choices under provider, 

1. Optional
2. Required
3. Requisite
4. Sufficient

This post we can discuss on the four types of flags and how authentication is carried when each one of them is set to true. 

Optional - In optional setting, either of the two providers should have the username/password used to login. The authentication should return success in either one of them. Authentication provider is not always called. If the first authentication provider returns true then authentication ends there. 

Required - Required is the default option configured in weblogic, when you have no other providers other than weblogic provider. In this type of control flag, the user must pass authentication in all providers. If there are 5 providers with control flag, 

Requsite - User must pass authentication test with the particular provider. Other providers are also tested. 

Sufficient- When you create a new provider to existing LDAP, user required to pass at atleast one provider. If one is a successful authentication, then authentication is completed.  

Note: For existence of Custom LDAP and obiee realm, SUFFICIENT' should be opted for. 

Importance of creating separate schema for Work in ODI

We had come across an article a long while back on the significance of using a separate schema for work instead of Target schema in ODIExperts blog. We later learned from this as we could clearly see the advantage of doing the same. 

We use OBIEE as our front end tool and we could see that all the work tables of ODI for each target table would be present in the target schema. The more tables in the target schema, the more time it takes for OBIEE to process the import metadata step. Imagine you have 10 tables in your DWH target schema, if you don't define a separate work schema for each table then for one table, W_STUDENT_D you will have the following work tables also, 

C$_W_STUDENT_D, 

I$_W_STUDENT_D,

E$_W_STUDENT_D

This not only makes OBIEE import slow also it takes up table space on the target schema making database management hard. If maintained separately then you can purge all tables in the work schema at once. The worry of accidentally truncating any of your target tables are also eliminated this way. 

Another important step as they discussed is to not use the 'Stage area different from target option', which is done at the interface level. The same can be done at the physical schema in topology level, 

ODI Work Schema.

 

Using ODI Procedures to load data from Source to Target

We came across a scenario where we used ODI procedure to load data from source to target. The scenario was that we had to implement a very complex logic using sub queries in an ODI Interface. given the short timeline, we went ahead with the approach of loading the same using query in an ODI Procedure. We will discuss the same below,

An ODI Procedure has two parts, Command on Source and Command on Target,

• ODI Command on Source is where you write the source SQL query that you use to fetch data from source. 
• ODI Command on Target is where you write the insert script to load data into the target. 
• Load data from one source to another source (eg: Oracle to Teradata, MSSQL to Oracle, etc)
• The source side query can be a plain query. The number of columns in select though should match with the number columns in the insert script and their datatypes also should match. 

 eg: SELECT ROLL_NO,STUD_NAME FROM STUDENT

• The insert script should contain bind variables to select values from the source query, the insert script for the above source query would be, 

  INSERT INTO W_STUDENTS_D VALUES (:ROLL_NO,:STUD_NAME)

• Extremely inefficient compared to interfaces.
• Never preferred over interfaces. Interfaces are always faster as ODI follows all standards while generating queries. Preferred for loading of tables with less volumes of data.'
• Row by row processing happens and hence it takes longer time for data load. 

Typically the errors you get are either SQL syntax errors, Data type mismatch, column size errors, column order mismatch errors. 

All about Oracle Data Integrator(ODI) Variables

ODI variable is an ODI object that can be used to store values of string, date or number datatypes. Variables can be both static and dynamic, they can be updated at run-time using a query. These variables can be passed to other ODI objects like interfaces or procedures as filters or as column values.

• Can store String/Date/Number values.
• Values are updated during run-time.
• Values can be passed to other ODI objects like Procedures and Interfaces.
• Can either be Static/Dynamic in nature. 
• Dynamic ODI variables can be refreshed with a SQL query executed against a source database.
• Variables are commonly used for filter conditions, case conditions etc in interfaces, procedures and packages.

The scope of an ODI variable is two, 

Global: The variable can be called across projects in ODI repository.

Project: The scope of the ODI variable is limited to the particular project. 

There are three attributes to an ODI variable, 

1. Name
2. Datatype
3. Action

Action determines how values are to be stored in an ODI variable, The action types are 

Persistent: Values are stored for a particular session

Last Value: Stores the last value. 

Historize: Stores current the value and the others in history. Very useful if your variable values change frequently over time. Helps in debugging. 

An ODI variable can be called using the below syntax,

#PROJECT_NAME.Variable_Name

ODI used scenario examples, 

1. Imagine you are passing a date value dynamically from database to fetch records for ETL load. Instead of using a sub query filter, you can fetch the date value to a variable and use it in all your procedures and interfaces. 
2. Another scenario is storing JDBC url. You might be using JDBC urls at different places in an ODI instance. If hardcoded at each place, it would be hard to find and map the change wherever the same JDBC url has been used. Instead you can save the JDBC url as an ODI variable and then use the variable name wherever needed in topology. 

Oracle has a pretty neat document on variables. 

Dimensional Modelling - Loan Dimension in Banking Data warehouse - Part 1

Loan Dimension in a Banking Enterprise Data warehouse is very crucial. It is undoubtedly one of the if not biggest data warehouse dimension in a banking DW architecture. It is also of one of the most confirmed dimensions in an banking data warehouse. Loan dimension has a lot of textual attributes and also an almost equal number of measurable attributes. It is also joined to many other entities of a banking EDW. A few examples of the key entities are the following,

• Branch 
• Primary Customer
• Teller
• Loan Product
• Time/Dates ( Maturity, Commencement, Settlement, Disbursement, Dormancy )

As we discussed before there are many textual attributes are unique to a loan dimension and few examples are listed below,

Account status,
Product Name
Settlement, Account open date, Loan Approved date, First disbursement date, Last Transaction date.

Considering the size of a loan dimension it can be split into multiple tables to improve performance and maintainability. A good way to start modelling a dimensional model is to create bus matrix as suggested by Ralph Kimball.

The bus matrix for a Loan dimension in an ideal scenario would look as below,

Bus Matrix for Loan Dimension.

The loan dimension being one of the largest entities will take up a number of columns. It is a good practice to split the dimension as follows in case it has a large number of table,

The first partition of the dimension table should have all the most commonly used attributes, like Status, Balance etc. The second partition should have all the measurable attributes like Approved amount, applied amount, and the corresponding date keys. These measures can also act as attributes in some cases. The second table can also be used as a fact in some cases. The third or the next partitions should have all the junk values related to the dimension. Advised to put all the rarely used dimension columns here. These tables can be made to one single dimension in BI tool for example with the help of Logical table sources in OBIEE.

Why we are advising this strategy is because otherwise the maintenance of this table would be down the line a headache. Imagine a table with 500 columns. A single insert statement would be of large size. Hence taking more time to execute and load into target schema.

Naming conventions, You can go ahead with standard naming conventions followed by Oracle or have one of your own. An Oracle naming standard would be,

W_LOAN_D

If you have multiple sources, Then you can have a source abbreviation also in the same name.