Synapse and Data Factory architecture scope
Azure Data Factory orchestrates ingestion, scheduling, dependency control, and hybrid connectivity, while Azure Synapse provides dedicated SQL, serverless analytics, Spark, and workspace-level governance for large-scale data processing. Together they form a repeatable enterprise pattern for landing raw data, curating trusted data products, and serving governed analytics consumers.
Pipeline orchestration and transformation design
Data Factory should manage extraction contracts, parameterized pipelines, runtime monitoring, and environment promotion controls. Synapse then handles warehouse, Spark, and lake query workloads that align with curated and serving zones. Architecture quality depends on schema versioning, workload isolation, and metadata-driven design rather than one-off pipeline logic.
Related pathways: Microsoft Azure Data Platform Architecture, BI and Analytics Hub, ETL ELT CDC patterns.
Analytics consumption and governance
Synapse outputs should align to semantic governance, KPI definitions, and controlled access paths for finance, operations, and executive reporting. Teams should pair monitoring, lineage capture, and cost observability with workload prioritization so analytics scale does not degrade reliability or create uncontrolled spend.
Platform governance alignment: Data Integration Hub, Cloud Infrastructure Hub, and Cybersecurity Hub.
Hub pathways
Return to Data Integration taxonomy, continue to BI and Analytics strategy, or review Modern Data and Integration Stack Architecture.