data federation

After describing the context in which we apply data federation, we discuss the problem that this pattern addresses, as well as the solution.

Data federation strives to leave data in its original location and to create what can be thought of as a virtual database.

Implementations of the data federation pattern typically support at least one of those models, sometimes both.

Cache and data replication patterns can be used to supplement the data federation or in the creation of a composite pattern.

The data federation pattern focuses on the integration of data sources and provides a single system image through a data-oriented interface.

This is due to the real-time, synchronous nature of responding to requests in the data federation pattern.

This capability is a prerequisite to fully achieving the level of collaboration and data federation enabled by the LSID Resolution Network.

As described in the value statement, data federation can greatly improve delivery time when integrating various sources.

In many cases, the data federation server has very similar characteristics than a database or content server.

It is important that we continue to enhance the data federation technology while working closely with customers.

We will conclude by summarizing when to apply and when not to apply the data federation pattern and to list important constraints.

The data federation pattern virtualizes data from multiple disparate information sources.

Data federation joins the information from multiple sources so that it can be surfaced as a service to the consumer.

The top concern with data federation is performance.

With data federation, the consumer will see a single uniform interface.

This article highlights the value of the data federation approach.

In most cases, the data federation pattern is used for read access because of the complexity of write access.

The data federation server receives a request to the integrated view.

The data federation server is responsible for receiving a query directed at an integrated view of diverse sources.

Implementations of the data federation pattern vary in their capabilities which specific source models they can integrate.

In today's information-driven environment it is very common for architects and developers to implement a data federation solution.

The data federation server can consume -- in order to integrate -- services provided by multiple information sources.

The data federation pattern allows for real-time, integrated access to sources with the highest level of data currency.

There is a big push toward information integration and on-demand computing through data federation and autonomic computing.

Data federation gives you a way to integrate the diverse data in your enterprise.

With more and more customers adopting data federation, the challenges of enabling seamless federation continue to grow.

On the other hand, data federation increasingly requires data transformation capabilities to improve data quality and flexibility.

Traditional data federation technology, such as ETL, data warehouse, and so on.

An application that is a consumer of the data federation server can interface with a single virtual data source.

Focus areas to apply the data federation pattern