Federated search provides one interface to query across silos, with two main approaches. Search-time merging creates and maintains separate indexes for each data source, offering fast implementation, varied data coverage, and up-to-date results but suffering from slower response, poor user experience, and weaker relevance. Index-time merging builds a unified index from all sources, delivering better relevance, modern search UX, faster responses, and the ability to include data without a native search engine. However, it requires slower implementation and updates. Combining index-time merging with machine learning AI ensures the best balance of speed, accuracy, and user experience.

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