Answer Posted / sreenu329
One of the most interesting and valuable dimensions in a
data warehouse is one that explains why a fact table record
exists. In most data warehouses, you build a fact table
record when something happens. For example:
When the cash register rings in a retail store, a fact
table record is created for each line item on the sales
ticket. The obvious dimensions of this fact table record
are product, store, customer, sales ticket, and time.
At a bank ATM, a fact table record is created for every
customer transaction. The dimensions of this fact table
record are financial service, ATM location, customer,
transaction type, and time.
When the telephone rings, the phone company creates a fact
table record for each "hook event." A complete call-
tracking data warehouse in a telephone company records each
completed call, busy signal, wrong number, and partially
dialed call.
In all three of these cases, a physical event takes place,
and the data warehouse responds by storing a fact table
record. However, the physical events and the corresponding
fact table records are more interesting than simply storing
a small piece of rev enue. Each event represents a
conscious decision by the customer to use the product or
the service. A good marketing person is fascinated by these
events. Why did the customer choose to buy the product or
use the service at that exact moment? If we only had a
dimension called "Why Did The Customer Buy My Product Just
Now?" our data warehouses could answer almost any marketing
question. We call a dimension like this a "causal"
dimension, because it explains what caused the event.
| Is This Answer Correct ? | 18 Yes | 0 No |
Post New Answer View All Answers
What do you mean incremental aggregation?
What are the types of caches in lookup? Explain them.
What are the differences between a connected lookup and unconnected lookup?
Explain what is informatica metadata and where is it stored?
What is a repository manager?
What is the difference between power center and power mart? What is the procedure for creating independent data marts from informatica 7.1?
Quickly characterize reusable change?
5. Consider the following products data which contain duplicate rows. A B C C B D B Q1. Design a mapping to load all unique products in one table and the duplicate rows in another table. The first table should contain the following output A D The second target should contain the following output B B B C C
What are the different lookup cache(s)?
How to load the data from a flat file into the target where the source flat file name changes daily?
As union transformation gives UNION ALL output, how you will get the UNION output?
Please let me know how to make Data masking in informatica..
Debugger what are the modules, what are the options you can specify when using debugger, can you change the expression condition dynamically when the debugger is running.
What happen when you enable grid option avilable at session level ? (Ans found: Scalabily . A single session Parallelization) But how can one session is sharable among different nodes at the same time while running ?
What are the new features of informatica 9.x developer?
