This lesson develops practical capability in Azure Data Architecture, Data Lakes, Synapse Workspaces, Ingestion, and Integration as part of Extended Data Engineering on Azure with Azure Synapse, Microsoft Purview and Power BI. Learners connect the concepts to a realistic workplace objective, perform guided work, validate evidence, and document improvements.
The lesson begins by defining the architecture, stakeholders, information flow, operating assumptions, and quality expectations that shape azure data architecture, data lakes, synapse workspaces, ingestion, and integration. It distinguishes essential decisions from optional enhancements and explains how security, governance, usability, cost, and supportability influence the final design.
Learners then map the end-to-end workflow, identify control points, and choose appropriate configuration or analysis steps. Special attention is given to dependencies, permissions, data boundaries, exception handling, documentation, and the evidence required for an accountable operational handoff.
Apply the lesson to a scenario in which an organization must improve building governed Azure data engineering solutions with Azure Synapse, Microsoft Purview, and Power BI through ingestion, data lakes, SQL, Spark, pipelines, modeling, cataloging, lineage, semantic models, reporting, security, performance, monitoring, and operational validation. Produce a concise plan, implementation or analysis evidence, a validation checklist, a troubleshooting record, and a prioritized recommendation for the next iteration.