Federated search is a way of querying security data where it already lives, across several data stores, clouds or regions, and combining the results, instead of first copying everything into one central system. An analyst writes one search; the platform sends it to each location and brings the answers back together.
Federated search avoids moving data and works across boundaries, but each search depends on the speed of the slowest store, and fields may not line up if each store uses a different format. Centralizing data gives fast, consistent searches and easier correlation, but costs more to move and store and may conflict with residency rules.
Most mature designs combine the two: centralize the data that drives detection and frequent investigation, and federate the rest. Normalizing to a common schema such as OCSF makes either approach work much better.
Is federated search slower?
It can be, because results depend on each underlying store. It works best for investigations and hunts rather than real-time detection.
Does federated search need a common schema?
It does not strictly need one, but without it, the same search has to be written differently for each store and results are hard to combine.
The Anomali Unified Security Data Lake keeps normalized, enriched data searchable at scale and can work alongside existing data platforms such as Databricks and Snowflake.
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