A screenshot of a computer Description automatically generated

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.

Data Analysts ' Access Requirements must only have read access to the Delta tables in the gold layer and not have access to the bronze and silver layers.

The gold layer data is typically queried via SQL Endpoints. Granting the Read all SQL Endpoint data permission allows data analysts to query the data using familiar SQL-based tools while restricting access to the underlying files.

To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:

Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.

Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.

The technical requirements specify that:

Medallion layers must be fully populated sequentially (bronze → silver → gold). Each layer must be populated before the next.

If any step fails, the process must notify the data engineers.

Data imports should run simultaneously when possible.

Why Use a Data Pipeline That Calls Other Data Pipelines?

A data pipeline provides a modular and reusable approach to orchestrating the sequential population of medallion layers.

By calling other pipelines, each pipeline can focus on populating a specific layer (bronze, silver, or gold), simplifying development and maintenance.

A parent pipeline can handle:

- Sequential execution of child pipelines.

- Error handling to send email notifications upon failures.

- Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).