Rachel - I always find it helpful to understand something if I know the
origins. I worked with SAS several years ago. At that time it was a
statistical analysis package. A scientist or engineer could load a set of
test data into it and perform various arithmetic and statistical analyses.
Today most of that can be done with Oracle or MS Excel. My point is that I
would expect it to be heavily biased toward mathematical capabilities. Like
Data Mining, which is all statistics. Learn what that term means.
To learn Data Warehousing, I would encourage you to just do some
"Googling" and find good tutorials. An excellent newslist is dwlist.
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The magazine http://www.intelligententerprise.com/ has some excellent
information. I would search for "Ralph Kimball". He is one of the leading
figures in the DW arena. Look for some of his earliest columns on the
magazine site. He also answers questions on dwlist from time to time.
The main change you need yourself is to forget normalization. DBAs that
can't get past that point don't last long in the DW field. In the early days
the DW people would patiently explain the reasons to a DBA, but today there
are enough DBAs that have made the leap that a hard-headed normalization
bigot just isn't tolerated. It is much easier to just ask for a replacement
DBA.
The reason normalization isn't adhered to in DW is that users will
be creating their own queries and they can't understand 10-table joins with
outer joins, etc. A DW is usually loaded and then queried. Our DW is loaded
each weekend and then queried all week. So a DW is deliberately denormalized
and contains redundant data for ease of use.
OLTP databases have no concept of "time". A DW is all about time. To
reconstruct what the situation is at various points of time, the DW has
loads of historical data. For example, marketing people need to be able to
reconstruct the amount of business they did with a customer over a period of
time last year and compare it with the same period this year.
So between denormalization and tons of detailed historical data, DWs
are normally BIG! Fortunately they are usually read-only.
For Oracle, you want Enterprise Edition with the partitioning
option. And study Oracle Materialized Views.
In schema, a DW is usually a central fact table and 4-6 dimension
tables. Less than 4 dimensions and you don't need a DW. More than 6 and
marketing people can't understand the model. Normally the fact table is much
larger than the others, but not always. One of Wal-Mart's dimension tables
is each person in the U.S. Just size each of those tables, and you've got
your size. Growth is easy to predict. Ralph Kimball warns that often people
will get the grain wrong. They will size it for data summarized at the
weekly level, then after it is built they will realize that isn't going to
cut it and need a daily level. You must start almost from scratch and get 7
times the disk capacity. That is the fun side of being a DW DBA. Your
cynical instincts will still serve you well, just get them away from
normalization and worry about getting the grain right.
Okay, I've rambled along here too long. Hope that gets you off on
the right foot.
-----Original Message-----
[mailto:[EMAIL PROTECTED]]
Sent: Friday, May 03, 2002 5:08 PM
To: Multiple recipients of list ORACLE-L
Okay, my background is OLTP, but we are looking at a data warehousing
project
here....
any and all help appreciated! Specifically:
1) does anyone have any experience with a product called "SAS
Datawarehousing
Administrator" (or SAS)?
2) how do I go about doing rough estimates of sizing needs, assuming I will
get
rough numbers of information being collected, growth rates, length of
history to
keep, etc.
help?
Rachel
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