Microsoft PL-300 Bundle

Hybrid partitions combine cached older data and source-query access to recent data. The boundary it addresses is “query newly arrived fact rows in real time”, while this case concerns “SQL sales data rather than live source changes”.

DirectQuery retrieves results from the source on report queries and inherits source latency. This solves “database without copying fact rows into the model”; the stated issue is “SQL sales data rather than live source changes”.

Direct Lake accesses Delta-backed data through semantic-model caching without the usual Import refresh pipeline. Its application is “analyst wants near-current data without conventional imported-copy refresh”, not the need “SQL sales data rather than live source changes”.

Direct Lake accesses Delta-backed data through semantic-model caching without the usual Import refresh pipeline. That is a different operating condition from “database without copying fact rows into the model”: “analyst wants near-current data without conventional imported-copy refresh”.

Hybrid partitions combine cached older data and source-query access to recent data. It serves “query newly arrived fact rows in real time”, not “database without copying fact rows into the model”.

Live-connected reports reuse the upstream model rather than separately importing the same facts. The present requirement is “database without copying fact rows into the model”; “model's measures and governance” is a distinct situation.

Import uses in-memory VertiPaq data for fast interaction, but requires refresh for new rows. The present requirement is “analyst wants near-current data without conventional imported-copy refresh”; “SQL sales data rather than live source changes” is a distinct situation.

Hybrid partitions combine cached older data and source-query access to recent data. That matters for “query newly arrived fact rows in real time” rather than the condition “analyst wants near-current data without conventional imported-copy refresh”.

DirectQuery retrieves results from the source on report queries and inherits source latency. The boundary it addresses is “database without copying fact rows into the model”, while this case concerns “analyst wants near-current data without conventional imported-copy refresh”.

DirectQuery retrieves results from the source on report queries and inherits source latency. This solves “database without copying fact rows into the model”; the stated issue is “query newly arrived fact rows in real time”.

Direct Lake accesses Delta-backed data through semantic-model caching without the usual Import refresh pipeline. The deciding difference is “analyst wants near-current data without conventional imported-copy refresh” versus “query newly arrived fact rows in real time”.

Live-connected reports reuse the upstream model rather than separately importing the same facts. That is a different operating condition from “query newly arrived fact rows in real time”: “model's measures and governance”.

Hybrid partitions combine cached older data and source-query access to recent data. That is a different operating condition from “model's measures and governance”: “query newly arrived fact rows in real time”.

DirectQuery retrieves results from the source on report queries and inherits source latency. It serves “database without copying fact rows into the model”, not “model's measures and governance”.

Direct Lake accesses Delta-backed data through semantic-model caching without the usual Import refresh pipeline. The present requirement is “model's measures and governance”; “analyst wants near-current data without conventional imported-copy refresh” is a distinct situation.

JSON-oriented ingestion preserves nested objects until they are expanded into tabular columns. The boundary it addresses is “the analyst must expand into fields”, while this case concerns “tables and should be imported into Power Query”.

Text/CSV import parses delimited records rather than treating each file as an Excel workbook. This solves “text with a header row and encoding requirements”; the stated issue is “tables and should be imported into Power Query”.

Folder ingestion applies a common transformation to matching files without manually importing each file. The deciding difference is “must be ingested together” versus “tables and should be imported into Power Query”.

Folder ingestion applies a common transformation to matching files without manually importing each file. Its application is “must be ingested together”, not the need “text with a header row and encoding requirements”.

The SQL Server connector supports database navigation and server-side query execution. That is a different operating condition from “text with a header row and encoding requirements”: “database and can fold filtering to the server”.

The Excel connector understands workbook navigation items and table structures. It serves “tables and should be imported into Power Query”, not “text with a header row and encoding requirements”.

Folder ingestion applies a common transformation to matching files without manually importing each file. It serves “must be ingested together”, not “database and can fold filtering to the server”.

Text/CSV import parses delimited records rather than treating each file as an Excel workbook. That matters for “text with a header row and encoding requirements” rather than the condition “database and can fold filtering to the server”.

JSON-oriented ingestion preserves nested objects until they are expanded into tabular columns. The boundary it addresses is “the analyst must expand into fields”, while this case concerns “database and can fold filtering to the server”.

The Excel connector understands workbook navigation items and table structures. The boundary it addresses is “tables and should be imported into Power Query”, while this case concerns “the analyst must expand into fields”.

Text/CSV import parses delimited records rather than treating each file as an Excel workbook. This solves “text with a header row and encoding requirements”; the stated issue is “the analyst must expand into fields”.

The SQL Server connector supports database navigation and server-side query execution. Its application is “database and can fold filtering to the server”, not the need “the analyst must expand into fields”.

The Excel connector understands workbook navigation items and table structures. That is a different operating condition from “must be ingested together”: “tables and should be imported into Power Query”.

JSON-oriented ingestion preserves nested objects until they are expanded into tabular columns. It serves “the analyst must expand into fields”, not “must be ingested together”.

Text/CSV import parses delimited records rather than treating each file as an Excel workbook. The present requirement is “must be ingested together”; “text with a header row and encoding requirements” is a distinct situation.