Which describes an end-to-end data platform design for a SaaS company in Fabric?

• A. Ingest from SaaS APIs via pipelines into OneLake lakehouse; store raw and curated layers; transform via ELT with Spark and SQL; apply RBAC, lineage, and data masks; enable BI via Power BI and notebooks via Data Science workspace.
• B. Ingest from FTP to local storage; transform manually; skip governance.
• C. Use external S3 bucket without OneLake integration.
• D. Only build dashboards without data processing.

Answer: (A)

End-to-end data platform design in Fabric for a SaaS company combines automated ingestion, centralized storage, scalable processing, governance, and broad consumption paths. In this approach, data from SaaS APIs is ingested via pipelines into the OneLake lakehouse, with a raw layer for the original data and a curated layer for business-ready content. Transformations are performed using ELT with Spark and SQL, leveraging the lakehouse for scalable, near-real-time processing. Governance is built in through RBAC, data lineage, and data masking to enforce security, compliance, and traceability. Finally, data is consumed by BI tools like Power BI for dashboards and by the Data Science workspace for notebooks, satisfying both reporting and advanced analytics needs. This combination provides a cohesive, scalable, and secure workflow from source to insight.

The other options miss essential elements: automated ingestion with a proper storage and processing path, governance, or Fabric integration, or they stop at dashboards without data processing.