Key Challenges in Power BI
- Performance Bottlenecks: Power BI’s native language is DAX, and its DirectQuery mode has trouble translating DAX into SQL inefficiently.
- Inability to Handle Large Datasets:
- Import Mode requires data to be imported and stored locally in Power BI servers’ memory, leading to data size constraints.
- Power BI’s Direct Lake option also struggles with scalability, defaulting to the slower DirectQuery mode for datasets that exceed modest data size thresholds.
- Inconsistent Metrics: End users can create semantic models at the Power BI workbook level, leading to conflicting and inconsistent KPIs.
- High Costs: Inefficient cloud usage for Direct Query mode increases data platform costs, while Direct Lake and Import mode leads to data duplication, additional processing and storage costs.
- Security and Governance: Ensures compliance with data access policies through row- and column-level governance and integrates with LDAP/Active Directory for secure, role-based access control.
AtScale: The Semantic Layer Advantage
AtScale bridges Power BI with cloud data platforms (e.g., Snowflake, Databricks), providing:
- Live Query Access: Eliminates data imports and ensures real-time insights.
- Optimized Performance: Native DAX support avoids query translation delays, delivering 4x faster queries.
- Consistent Metrics: Standardizes business definitions in centrally managed semantic models, ensuring accuracy across tools and teams.
- Enhanced Governance: Enforces row and column-level security with enterprise directory integrations.
Power BI Use Cases Powered by AtScale
- Enterprise-Scale Analytics: Analyze billions of rows without pre-aggregation.
- Self-Service BI: Empower non-technical users with intuitive, consistent, business-friendly models through self-service BI, eliminating the need to model data at the workbook level.
- Cost Optimization: Reduce cloud computing and storage costs by over 3x.
