Current Approach Assessment

• Power BI Import Mode: Importing all table data results in full dataset refreshes, driving up compute and data transfer costs during each refresh.

• Delta Lake as Source: Databricks clusters are used for both ETL and responding to Power BI refresh queries. All-purpose clusters can be expensive since they are high-performing and designed for interactive workloads.

• SQL Warehouse Attempt: Switching to Databricks SQL Warehouse did not reduce your refresh costs as much as expected. Real-world workloads may not always match the advertised savings, especially with frequent large imports and complex star schemas.

• Incremental Refresh: This can reduce refresh time and cost, but introduces complexity (e.g., PBIX download restrictions and additional setup).

• Azure SQL Alternative: Loading data from Delta to Azure SQL Database could allow Power BI to connect using a more BI-tuned engine, but incurs its own charges and transfer overhead.

• DirectQuery Option: This could reduce Power BI refresh costs by delegating query execution to Databricks on-demand, but might introduce latency and increased compute cost on cluster side with every report/filter interaction.

Recommended Alternatives

1. Optimize Databricks Cluster Usage

• Use Databricks SQL Warehouses rather than all-purpose clusters for Power BI connections. They are optimized for BI workloads and billed based on usage and capacity units, not cluster uptime.

• Adjust warehouse size and auto-stop settings to avoid unnecessary compute costs.

2. Consider Incremental Refresh in Power BI

• Incremental refresh lowers refresh costs and improves performance by updating only new/changed data. If PBIX file access is critical, use deployment pipelines to maintain access to the source file and separate development from production.

• Carefully partition your tables by date or ID to maximize incremental refresh benefits.

3. Explore DirectQuery Mode

• DirectQuery sends queries to Databricks only when users interact with reports, potentially reducing overall Power BI refresh frequency and costs.

• However, it can increase Databricks compute at query time and may impact report responsiveness, especially with large or complex models.

4. Offload Aggregations to ETL/SQL Warehouse

• Pre-aggregate or flatten data during your Databricks ELT process, reducing the data size and complexity Power BI ingests.

• If feasible, expose aggregate or summary tables in Gold layer for reporting.

5. Hybrid Azure SQL Solution

• If Azure SQL Database is affordable for your volumes, consider moving only critical reporting tables from Delta Lake to SQL.

• Use Databricks for transformation and Azure Data Factory or native features to copy results.

Cost Management Tips

• Monitor all-purpose vs. SQL Warehouse vs. Azure SQL costs and query volumes to guide future decisions.

• Use Power BI Premium for larger datasets and advanced incremental refresh, which may further optimize costs in some scenarios.

• Tag clusters/warehouses and analyze usage patterns to reduce unnecessary spend.

Summary Table: Power BI + Databricks Cost/Performance Options

Final Recommendations

• Begin with Databricks SQL Warehouses and optimize Power BI refresh strategy with incremental loading.

• Test DirectQuery for specific reports where real-time data is critical and cost is less sensitive.

• Aggregate your data as much as possible before loading into Power BI.

• Monitor/compare actual costs per approach at scale; each environment's cost profiles vary.

This should help you choose the best combination for your workflow, balancing cost, performance, and reporting needs.