Almost all conventional federated data systems have to rely on adapters and/or middleware to submit queries to data sources for execution

Data remains in data sources and queries are translated from a standard 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, which operate as illustrated in the following diagram CFA1:

CFA1: Conventional federated adapter-based data access solution

Advantages

Conventional federated 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 primary 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 conventional federated 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 systems

• 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 federated adapters are developed, typically, over a significant period and at great cost to cover basic requirements.  It typically costs 300 to 500% of the initial adapter purchase cost to customize conventional adapters to cover basic requirements, never mind advanced or future 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.