Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
EDW Training 1
EDW Training 2
EDW Training 3
EDW Training 4
EDW Training 5
EDW Training 6
EDW Training 7
EDW Training 8
EDW Training 9
EDW Training 10
EDW Training 11
Designed as a Data Source to Ease Ad-Hoc Reporting
EDW Training 12
Organized into Simplified Business Concepts
Improves Information Access Performance
Source to Enterprise Data Warehouse
Ensures Consistent Reporting Results
Common Data Source
Common Business Concepts
Cognos FM model and packages
EDW Training 13
EDW Training 14
EDW Training 15
EDW Training 16
EDW Training 17
EDW Training 18
EDW Training 19
EDW Training 20
EDW Training 21
EDW Training 22
EDW Training 23
EDW Training 24
EDW Training 25
EDW Training 26
EDW Training 27
EDW Training 28
EDW Training 29
EDW Training 30
EDW Training 31
EDW Training 32
EDW Training 33
EDW Training 34
EDW Training 35
EDW Training 36
EDW Training 37
EDW Training 38
EDW Training 39
EDW Training 40
EDW Training 41
EDW Training 42
EDW Training 43
EDW Training 44
EDW Training 45
EDW Training 46
EDW Training 47
EDW Training 48
EDW Training 49
EDW Training 50
EDW Training 51
EDW Training 52
EDW Training 53
EDW Training 54
EDW Training 55
EDW Training 56
EDW Training 57
EDW Training 58
EDW Training 59
EDW Training 60
EDW Training 61
EDW Training 62
EDW Training 63
EDW Training 64
EDW Training 65
EDW Training 66
EDW Training 67
EDW Training 68
1. Extract data from the ODS based upon parameters passed from the
EDW Training 69
Administrative UI. This data is loaded into the INPUT table associated with the
business area being loaded.
2. Load information within the INPUT table to the associated CLEAN table and run
the cleansing process. The cleansing process uses values defined by the institution
within the Administrative UI to manage descriptions and translate codes to then
update them in the CLEAN table.
3. Data from the CLEAN table is then used to discern the unique combinations of
dimensional attributes within the data extracted. New combinations of attributes are
inserted into their associated dimension tables and assigned a surrogate key. The
first dimension analyzed is the time dimension. If the combination of dimensional
attributes within the time dimension already exists, the loading process halts unless
the Replace Indicator checkbox is checked. This ensures that historical data is not
overridden unless explicitly requested by an institution.
4. After loading the attributes into the dimension tables, join the CLEAN table with
its various associated dimension tables to obtain the surrogate keys associated with
each record. This data is loaded into the associated WKEYS table.
5. Run the FACT_DELETE mapping to delete records in the fact table for the
defined time slice when the Replace Indicator checkbox is checked.
6. Load data from the WKEYS table into the fact table.
EDW Training 70
EDW Training 71
EDW Training 72
EDW Training 73
EDW Training 74
EDW Training 75
EDW Training 76
EDW Training 77
EDW Training 78
EDW Training 79
EDW Training 80
EDW Training 81
EDW Training 82
EDW Training 83
EDW Training 84
EDW Training 85
EDW Training 86
EDW Training 87
EDW Training 88
EDW Training 89
EDW Training 90