Start Oracle 12c pluggable database : PDBORCL

So with a default installation of Oracle 12c, you get a container database as well as a pluggable database. The default name for the pluggable database is 'PDBORCL'.

For those of who you have installed Oracle Business Intelligence 12c would know that the metadata schemas for 12c cannot be installed on a container database. You will need a pluggable database to store these metadata schemas. With default installation you get a pluggable database by the object name 'PDBORCL'. So if you haven't written a database trigger statement to start your pluggable database on Oracle 12c what happens is that only the container database gets started.  The result is that the web service is not able to connect and gather metadata from these two schema's and startup fails. 

So here is how to check the status of your pluggable database and how to mount/open it. To check the status of the pluggable database, first open sqlplus,

once you have opened sqlplus login as the SYS user and once connected run the following query,

SELECT NAME, OPEN_MODE FROM V$PDBS;

Status of Pluggable databases.

This will give you the list of pluggable databases on your 12c installation. To startup or mount the same, you have to run the following command,

ALTER PLUGGABLE DATABASE ALL OPEN;
or
ALTER PLUGGABLE DATABASE PDBORCL OPEN;

Pluggable Database open command.

Now you can see that the status has changed to read write.

DW Concepts: What is a Factless Fact table ? Usage and Examples

So any new BI/ETL developer starting out his career would come across this term, Factless Fact. So what is a factless fact, Let us see with the help of a few examples,

A Factless fact table is defined by Ralph Kimball as,

Fact tables that have no facts but captures the many-to-many relationship between dimension keys.

A simplified definition would be

A table in Data warehouse capturing the relation between two or more entities and doesn't have any measurable quantities or facts.

Imagine the example of a school- class - student - stream enrollment scenario where one row captures information of a student enrolled to a particular class.

ROW_WID	CLASS_WID	STUDENT_WID	STREAM_WID
1	30	25	1
2	31	31	1

As you can see from the above table structure, this table captures or the grain( lowest level of detail ) is enrollment details of a particular student assigned to a class and a particular stream. The WID columns are nothing but surrogate keys of individual dimensions and ROW_WID column is the surrogate key of the fact. 

This table as you can see doesn't contain any measurable entities like Marks. This table acts as an entity to identify the students enrolled in a particular class. and stream We can create derived measures from this fact by taking count of individual ROW_WID items, for eg:

1. Taking count of students(STUDENT_WID) grouped by Class will give you the number of Students assigned to a class. 
2. Taking count of students(STUDENT_WID) grouped by Stream gives you the number of students under a single stream. 
3. Taking the count of classes(CLASS_WID) grouped by Stream gives you the number of Classes under a particular Stream.

The star schema diagram for this factless fact is below,

Student Enrollment Factless Fact Schema Diagram

Naturally you will doubt that same information can be obtained from other fact tables such as marks, This is true, however imagine a scenario where a student no longer attends school and has not attended exams. In such a case there are chances that his mark details are not present in the marks fact table. Hence you will not be able to get accurately the information of all students assigned to a class or stream. This is where a factless fact table is significant in a Data warehouse.  

Other similar examples of factless fact tables are, 

• Insurance - Coverage - Membership
• Sales - Product Promotion Details