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When using the Copy data activity in a data pipeline to move data from Snowflake to a Fabric warehouse, the process often involves intermediate staging to handle data efficiently, especially for large datasets or cross-cloud data transfers.

Staging involves temporarily storing data in an intermediate location (e.g., Blob storage or Azure Data Lake) before loading it into the target destination.

For cross-cloud data transfers (e.g., from Snowflake to Fabric), enabling staging ensures data is processed and stored temporarily in an efficient format for transfer.

Staging is especially useful when dealing with large datasets, ensuring the process is optimized and avoids memory limitations.

To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Fabric, a data pipeline is the most appropriate tool. A data pipeline in Fabric is designed to move data between various data sources and destinations, including on-premises databases like SQL Server, and cloud-based storage like Fabric warehouses. The data pipeline can handle the connection through an on-premises data gateway, which is required to access on-premises data. This solution facilitates the orchestration of data movement and transformations if needed.

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Bronze Layer: A pipeline Copy activity

The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and the ability to ingest the data directly into the Delta format in the bronze layer.

Silver Layer: A notebook

The silver layer involves extensive data cleansing (deduplication, handling missing values, and standardizing capitalization). A notebook provides the flexibility to implement complex transformations and is well-suited for this task.

The requirement specifies that Litware plans to make the book reviews available in the lakehouse without making a copy of the data. In this case, creating a shortcut in Fabric is the most appropriate solution. A shortcut is a reference to the external data, and it allows Litware to access the book reviews stored in Amazon S3 without duplicating the data into the lakehouse.

The sales data issue can be resolved by configuring incremental refresh for the dataflow. Incremental refresh allows for only the new or changed data to be processed, minimizing the amount of data transferred and improving performance.

The solution specifies that data older than one month never changes, so setting the refresh period to 1 Month is appropriate. This ensures that only the most recent month of data will be refreshed, reducing unnecessary data transfers.

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SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.

FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries using the queryinsights.long_running_queries view, which provides insights into queries that take longer than expected to execute.

WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.

AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause failures and need attention.

To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access disabled, you need to create a managed private endpoint. A managed private endpoint is a secure, private connection that enables services like Fabric (or other Azure services) to access resources such as databases, storage accounts, or other services within a virtual network (VNet) without requiring public internet access. This approach maintains the security and integrity of your data while enabling access to the Azure SQL database.

Semantic models in Microsoft Fabric are part of Power BI datasets and require refreshes to stay updated with the latest data.

Dynamically executing and monitoring the refresh progress requires a tool or approach that integrates with Fabric's capabilities for semantic models.

In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.

Litware from New York will be displayed at the top of the result set – Yes

The data is sorted first by Location in descending order and then by UnitsSold in descending order. Since "New York" is alphabetically the last Location, it will appear first in the result set. Within "New York", Litware has the highest UnitsSold (1000), so it will be displayed at the top.

Fabrikam in Seattle will have value = 2 in the Rank column – No

The row_rank_dense function assigns dense ranks based on UnitsSold within each location. In "Seattle":

Contoso has UnitsSold = 300 → Rank 1

Litware has UnitsSold = 100 → Rank 2

Fabrikam also has UnitsSold = 100, so it shares the same rank (2) as Litware.

Litware in San Francisco will have the same value in the Rank column as Litware in New York – No

The rank is calculated separately for each location. In "San Francisco":

Both Relecloud and Litware have UnitsSold = 500, so they share the same rank (1).

In "New York", Litware has the highest UnitsSold = 1000 → Rank 1.

Since ranks are calculated independently for each location, Litware in San Francisco does not share the same rank as Litware in New York.