During a pilot with smart shelves, sensors publish an inventory event every 5 seconds to an Event Hub. Regional merchandisers rely on a Power BI dashboard to reorder items as soon as stock is low. Today the dashboard reads from a table that an ETL job overwrites with an hourly timestamped snapshot. Executives say they still miss stock-outs by nearly an hour and do not want users to click Refresh. As the data analyst, which data-versioning technique will ensure the dashboard tiles update automatically within seconds of each new event?
Retain the hourly snapshot and instruct users to force a browser refresh when needed.
Configure a real-time streaming dataset that pushes each inventory event directly to the dashboard.
Switch to an overnight full batch load and email merchandisers a CSV extract of inventory levels.
Reduce the ETL schedule to every 30 minutes but keep the snapshot history for analysis.
A real-time streaming (push) dataset keeps an open connection between the data source and the dashboard so each incoming event immediately redraws the visual, meeting the seconds-level SLA. Shortening the ETL to 30 minutes, keeping manual refresh, or moving to a nightly batch still rely on point-in-time snapshots, leaving minutes or hours of latency and risking missed stock-outs.
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