Microsoft PL-300 Bundle

The bottleneck is presentation time, not semantic-model query execution. This solves “a custom visual consumes most of its delay”; the stated issue is “uncached and cached runs without noting the difference”.

Performance Analyzer separates query, display and other visual time costs. Its application is “visual spends most time running a DAX query”, not the need “uncached and cached runs without noting the difference”.

Executing the extracted query helps isolate model evaluation behavior. That is a different operating condition from “uncached and cached runs without noting the difference”: “slow visual's DAX request outside the report canvas”.

Drop unused transaction identifiers after confirming they are not needed for detail. That is a different operating condition from “of unique values used only for daily grouping”: “deduplication and greatly increases model size”.

Removing irrelevant rows reduces storage and refresh work. It serves “requirements cover only five years”, not “of unique values used only for daily grouping”.

Remove the unused Comment column from the loaded semantic model. The present requirement is “of unique values used only for daily grouping”; “a long free-text Comment column unused by reports” is a distinct situation.

Using only needed granularity reduces cardinality in analytical operations. That matters for “month grouping in dashboards but retains second-level precision” rather than the condition “a long free-text Comment column unused by reports”.

Drop unused transaction identifiers after confirming they are not needed for detail. The boundary it addresses is “deduplication and greatly increases model size”, while this case concerns “a long free-text Comment column unused by reports”.

Lower cardinality can improve VertiPaq compression when fine detail is not required. This solves “of unique values used only for daily grouping”; the stated issue is “a long free-text Comment column unused by reports”.

Remove the unused Comment column from the loaded semantic model. The deciding difference is “a long free-text Comment column unused by reports” versus “deduplication and greatly increases model size”.

Removing irrelevant rows reduces storage and refresh work. Its application is “requirements cover only five years”, not the need “deduplication and greatly increases model size”.

Lower cardinality can improve VertiPaq compression when fine detail is not required. It serves “of unique values used only for daily grouping”, not “deduplication and greatly increases model size”.

Remove the unused Comment column from the loaded semantic model. The present requirement is “month grouping in dashboards but retains second-level precision”; “a long free-text Comment column unused by reports” is a distinct situation.

Removing irrelevant rows reduces storage and refresh work. That matters for “requirements cover only five years” rather than the condition “month grouping in dashboards but retains second-level precision”.

Drop unused transaction identifiers after confirming they are not needed for detail. The boundary it addresses is “deduplication and greatly increases model size”, while this case concerns “month grouping in dashboards but retains second-level precision”.

Lower cardinality can improve VertiPaq compression when fine detail is not required. This solves “of unique values used only for daily grouping”; the stated issue is “requirements cover only five years”.

Remove the unused Comment column from the loaded semantic model. The deciding difference is “a long free-text Comment column unused by reports” versus “requirements cover only five years”.

Drop unused transaction identifiers after confirming they are not needed for detail. Its application is “deduplication and greatly increases model size”, not the need “requirements cover only five years”.

Aggregations can cache common results while detailed queries remain at the source. Its application is “and sum queries without importing every detailed record”, not the need “the base fact has billions of order lines”.

Aggregation optimization requires usable definitions, not just a smaller table. That is a different operating condition from “the base fact has billions of order lines”: “be used regardless of incorrect relationships and mappings”.

Aggregates can supplement rather than destroy event-grain analysis. It serves “entirely with monthly totals, then loses customer drillthrough”, not “the base fact has billions of order lines”.

Aggregates can supplement rather than destroy event-grain analysis. That matters for “entirely with monthly totals, then loses customer drillthrough” rather than the condition “Ensure aggregation grain supports all filters and grouping”.

Preaggregated data can answer common grouping queries without scanning all detail rows. The boundary it addresses is “the base fact has billions of order lines”, while this case concerns “Ensure aggregation grain supports all filters and grouping”.

Aggregation optimization requires usable definitions, not just a smaller table. This solves “be used regardless of incorrect relationships and mappings”; the stated issue is “Ensure aggregation grain supports all filters and grouping”.

Aggregation optimization requires usable definitions, not just a smaller table. The deciding difference is “be used regardless of incorrect relationships and mappings” versus “entirely with monthly totals, then loses customer drillthrough”.

Missing Product grain can prevent a summary from answering Product-filtered requests. Its application is “Ensure aggregation grain supports all filters and grouping”, not the need “entirely with monthly totals, then loses customer drillthrough”.

Preaggregated data can answer common grouping queries without scanning all detail rows. That is a different operating condition from “entirely with monthly totals, then loses customer drillthrough”: “the base fact has billions of order lines”.

Preaggregated data can answer common grouping queries without scanning all detail rows. That is a different operating condition from “and sum queries without importing every detailed record”: “the base fact has billions of order lines”.

Missing Product grain can prevent a summary from answering Product-filtered requests. It serves “Ensure aggregation grain supports all filters and grouping”, not “and sum queries without importing every detailed record”.

Aggregates can supplement rather than destroy event-grain analysis. That matters for “entirely with monthly totals, then loses customer drillthrough” rather than the condition “and sum queries without importing every detailed record”.

Missing Product grain can prevent a summary from answering Product-filtered requests. That matters for “Ensure aggregation grain supports all filters and grouping” rather than the condition “be used regardless of incorrect relationships and mappings”.

Aggregations can cache common results while detailed queries remain at the source. The boundary it addresses is “and sum queries without importing every detailed record”, while this case concerns “be used regardless of incorrect relationships and mappings”.

Preaggregated data can answer common grouping queries without scanning all detail rows. This solves “the base fact has billions of order lines”; the stated issue is “be used regardless of incorrect relationships and mappings”.