A BI analyst is troubleshooting a sales performance dashboard that connects to a cloud data-warehouse fact table containing five years (approx. 200 million rows) of row-level transactions. When business users open the dashboard, it regularly takes more than 60 seconds to load and returns incomplete totals because the warehouse kills any query that runs longer than 30 seconds. Stakeholders have confirmed they only need to see the current fiscal quarter's results during weekly meetings, and the analyst is not permitted to change the existing data model or measures. Which action that relies on data filtering will MOST effectively resolve both the long load time and the incomplete totals?
Disable the index on the transaction_date column so the warehouse performs full-table scans
Replace the existing stacked bar charts with line charts to reduce rendering overhead
Add a data-source or SQL WHERE clause that restricts the dataset to the current fiscal quarter before the dashboard executes
Export the dashboard as a static PDF and email it to stakeholders each week
Placing a filter at the data-source or SQL level so that only the current fiscal quarter is queried means the dashboard retrieves a tiny fraction of the original 200 million-row table. Fewer rows translate directly into lighter queries that finish within the warehouse's 30-second limit, so visual totals are complete, and the overall load time drops. Merely changing chart types does not meaningfully cut query cost; disabling an index would slow scans even further; exporting a static PDF bypasses interactivity rather than fixing the underlying performance and completeness issues.
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