A city UX team needs top pages, conversion funnels, and portal error rates without querying the raw clickstream lake each morning. Which serving choice best fits a municipal web analyzer?
Select an answer to reveal the explanation.
Short Explanation
Dashboards are the storefront, not the warehouse. UX folks need clear top pages, funnel steps, and error spikes without spelunking raw hits. Curated APIs and boards keep the lake for engineers and the answers for the people fixing the portal.
Full Explanation
A web analyzer typically separates collection and processing from serving. Product and UX stakeholders consume aggregated popularity, funnel, and reliability metrics through dashboards or APIs rather than ad-hoc lake queries. That serving layer turns Big Data pipelines into actionable civic UX insight while keeping raw event volume behind controlled aggregations.