Curators want to run ad hoc, exploratory queries over months of historical gallery-temperature telemetry — comparing trends across seasons, finding anomalies, and slicing by branch — using a query language built for fast time-series analytics at scale, rather than building a fixed routing topology. Which Fabric engine is purpose-built for this kind of analysis?

An Eventhouse is Fabric's Real-Time Intelligence data store, purpose-built to hold high-volume time-series and event data and to serve fast, flexible ad hoc analytics against it through KQL. KQL's query surface — filtering, summarize, time-binning, joins across tables — is designed exactly for the kind of exploratory, multi-month trend and anomaly analysis curators want, which makes the Eventhouse the right home for this workload rather than a live-routing tool.

An Eventstream with a Filter and Manage fields topology is a live-processing pipeline for shaping and routing data as it arrives; it is not a queryable store, and it has no mechanism for curators to run ad hoc historical queries after the fact. A Dataflow Gen2 with Power Query transformations is a batch data-preparation tool aimed at reshaping and loading data into a destination, not an interactive analytics engine curators can query directly and repeatedly. A Data Activator configured with a static threshold rule only watches for a single trigger condition to fire an action or alert; it has no query interface and cannot support open-ended exploration across seasons and branches.

A caveat: an Eventhouse's analytical strengths come with a different security and cost model than a Lakehouse, so the underlying telemetry may still need to be mirrored or shortcut into other Fabric items if broader reporting tools require it. Operationally, confirm fit by running a sample seasonal-comparison KQL query directly against the Eventhouse and checking response time against the volume of historical data involved.