Datasheet

Chapter 1: SSIS Solution Architecture
13
reporting of information to show trending and data summaries, the ETL part of a data warehouse is
important and critical.
Processing ETL for data warehousing involves extracting data from source systems or files, performing
transformation logic on the data (to correlate, cleanse, and consolidate), and then loading a data warehouse
environment (for reporting and analysis). Figure 1 - 6 shows common data-processing architecture for a
data warehouse ETL system.
Figure 1-6
ERP
Source Data
Execs, Managers,
Analysts, Accountants,
Engineers, Operations
HR/
CRM
Line of Business/
Other
Le
g
acy Exports
Staging
Data Mart/
Data Warehouse
Integration
Services
Cubes
Data Extraction and Transformation
Presentation data Queries
KPIs and
Dashboards
Reporting
Analytic
Tools
Data Processing and Storage Presentation
For those who are already versed in ETL concepts and practice, you may know that when it comes to
developing a data warehouse ETL system, moving from theory to practice often presents the biggest
challenge. Did you know that ETL typically takes up between 50 and 70 percent of a data warehousing
project? That is quite a daunting statistic. What it means is that even though presenting the data is the
end goal and the driving force for business, the largest portion of developing a data warehouse is spent
not on the presentation and organization of the data, but rather on the behind - the - scenes processing to
get the data ready.
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