Search

Top 60 Oracle Blogs

Recent comments

BI

Clean Data = Happy Analytics

I just finished cleaning up the example data that I was offered for my own demos and solutions. Working in Education requires you use education data to ensure what you’re presenting resonates with the users you’re working with. Otherwise you just look silly presenting something that makes utterly no sense to the individual you’re hoping to impress.

Having been given the gift a large data set from demos and solutions, I quickly took the data in its original form and attempted to use it, “as is” in Power BI. After a less than stellar demonstration, set off by bizarre results in my visuals, I chalked it up to my lack of experience with Power BI. Upon research, a different culprit appeared- incomplete, inaacurate and After all my years as a DBA, I should have known that it always goes back to the data. If you don’t have clean data and a clean data model, forget it. You’re just wasting your time.

Create constraints in your datawarehouse – why and how

We still see some developers not declaring referential integrity constraints in datawarehouse databases because they think they don’t need it (integrity of data has been validated by the ETL). Here is a small demo I did to show why you need to declare them, and how to do it to avoid any overhead on the ETL.

Test case

I create 3 dimension tables and 1 fact table:

21:01:18 SQL> create table DIM1 (DIM1_ID number, DIM1_ATT1 varchar2(20));
Table DIM1 created.
 
21:01:19 SQL> create table DIM2 (DIM2_ID number, DIM2_ATT1 varchar2(20));
Table DIM2 created.
 
21:01:20 SQL> create table DIM3 (DIM3_ID number, DIM3_ATT1 varchar2(20));
Table DIM3 created.
 
21:01:21 SQL> create table FACT (DIM1_ID number, DIM2_ID number, DIM3_ID number,MEAS1 number);
Table FACT created.