The stakeholder's requirement is for the dashboard to refresh at least every minute to reflect new data in the gold-level tables. In Databricks, refreshing a dashboard executes the underlying SQL queries on a SQL warehouse. Setting a refresh schedule to every minute means the SQL warehouse will be actively running queries continuously. This constant compute usage consumes Databricks Units (DBUs), which can lead to significant and potentially high operational costs. Therefore, it is a critical responsibility for the data analyst to caution stakeholders about the cost implications of such a high-frequency refresh requirement.

B. The gold-level tables are not appropriately clean for business reporting: This is incorrect. In the Medallion architecture, gold-level tables are specifically curated, aggregated, and cleaned for business intelligence and reporting purposes.

C. The streaming data is not an appropriate data source for a dashboard: This is false. A key use case for Databricks is to enable analytics and dashboarding on near real-time data that has been processed by a streaming pipeline and landed in a table.

D. The streaming cluster is not fault tolerant: This is incorrect. Databricks Structured Streaming is designed with fault tolerance as a core feature, ensuring data integrity and consistent processing even if failures occur.

E. The dashboard cannot be refreshed that quickly: This is factually incorrect. Databricks dashboards can be scheduled to refresh as frequently as every one minute, making the stakeholder's request technically feasible.

1. Dashboard Refresh Scheduling and Cost: Databricks Documentation

"Schedule a dashboard for periodic refreshes". This document shows that refresh intervals as low as "Every 1 minute" are available. It also notes

"When a dashboard is refreshed on a schedule

Databricks runs all queries in the dashboard. The user who saved the schedule is the one whose credentials are used to run the queries." This implies compute usage.

Source: Databricks Documentation > Databricks SQL > Dashboards > Schedule a dashboard for periodic refreshes.

2. SQL Warehouse Compute and Cost: Databricks Documentation

"What is a Databricks SQL warehouse?". This document explains that SQL warehouses are the compute resources for running queries. "The cost of a SQL warehouse is determined by its cluster size... and the number of clusters." Frequent queries directly translate to sustained compute usage and cost.

Source: Databricks Documentation > Databricks SQL > Administration > What is a Databricks SQL warehouse?

3. Gold-Level Tables: Databricks Documentation

"The medallion architecture". This guide defines the Gold layer as tables for "reporting and business intelligence" that are "often aggregated and contain broad

denormalized data."

Source: Databricks Documentation > Data management > Best practices > The medallion architecture.

4. Structured Streaming Fault Tolerance: Databricks Documentation

"Structured Streaming". The documentation states

"Structured Streaming provides fault tolerance and exactly-once data consistency for streaming queries."

Source: Databricks Documentation > Apache Spark > Structured Streaming.