2026 Guide: Real‑Time SAP to Databricks ETL Pipeline Blueprint

Real-time SAP-to-Databricks integration can improve decision-making, analytics, automation, and AI by making operational data quickly available in Databricks, but it requires careful planning around latency, schema changes, security, and data quality. The recommended approach begins by defining measurable business outcomes and KPIs such as freshness, processing latency, completeness, and integrity, then selecting a batch, event-driven, or hybrid architecture based on operational needs. The guide positions CData Sync as a low-code tool for connecting SAP and Databricks, supporting secure authentication, role-based access, governance, full loads, and timestamp- or integer-based incremental replication. It advises preparing SAP data and Databricks governance through accessible, standardized source tables and Unity Catalog, choosing suitable SAP change-data-capture methods such as ODP, SLT, or database logs, and establishing a clean historical baseline before transitioning to incremental updates. Ongoing validation should reconcile record counts, monitor freshness and latency, and verify relational consistency, while dashboards, alerts, logging, and scalable pipeline design help maintain reliable performance as data volumes and use cases expand.

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