What is a typical data engineering project lifecycle in Fabric from requirements to deployment?

• A. Gather requirements, design data model, build pipelines, implement governance, test and validate, monitor, and deploy with CI/CD and version control.
• B. Deploy without testing
• C. Design data model before gathering requirements
• D. No governance

Answer: (A)

A typical data engineering project lifecycle in Fabric follows a structured path from capturing requirements all the way to deployment, with governance and repeatable delivery practices. Start by gathering requirements to understand what data is needed, who will use it, and the quality and latency needs. Then design the data model to support those use cases, defining schemas, relationships, and data domains. Build the pipelines to ingest, transform, and store the data as required. Implement governance to establish data quality checks, lineage, access controls, and compliance. Test and validate to ensure correctness, performance, and reliability before moving forward. Monitor the production pipelines to detect issues and measure data freshness and quality. Deploy using CI/CD and version control to automate, track changes, and enable safe rollouts. This combination keeps delivery reproducible, auditable, and aligned with business needs.