| Data remains in data sources and queries are
translated from a common data model to queries that each data
source can execute. Queries are executed on, and results
retrieved from, data source systems. The components that
translate queries and transform result-sets are called adapters.
Figure 1: Federated data systems with conventional adapters diagram.
Advantages
Federated data systems with conventional adapters
were pursued in an attempt to overcome some of the disadvantages
of data warehouses by providing the following primary benefit:
 | Data remains at source |
The above benefit overcomes
many of the data warehouse challenges of:
| Complex
ETL process (time and cost) - see comment below* |
| Data ownership issues |
| Static and dated data |
| No drill-down
capabilities |
Disadvantages
However, there are
considerable disadvantages of federated data systems with
conventional adapters that
generally counter data warehouse advantages due to data source
constraints:
| Dirty data as is |
| Limited indexes not
consistent across data sources and not flexible |
| Limited query processing |
| Query load on data source
system |
| Query performance |
| Security |
| Data source owners aware
of queries (intelligence-related) |
To accommodate the translation
between an application or information sharing system and any
particular data source, conventional adapters are developed,
typically, over a significant period and at great cost to cover
basic requirements. In fact, it typically costs 300 to 500% of
the initial adapter purchase cost to customize conventional
adapters to cover basic requirements.
*The only advantage
conventional adapters have over the ETL process is that schema
transforms are not as difficult; however, query processing and
subsequent results transforms are more complex. |
