The advantages and disadvantages of federated data systems with conventional adapters

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:

bulletData remains at source

The above benefit overcomes many of the data warehouse challenges of:

bulletComplex ETL process (time and cost) - see comment below*
bulletData ownership issues
bulletStatic and dated data
bulletNo 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:

bulletDirty data “as is”
bulletLimited indexes – not consistent across data sources and not flexible
bulletLimited query processing
bulletQuery load on data source system
bulletQuery performance
bulletSecurity
bulletData 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.

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