Forum Discussion
Seeking Best Practices for End-to-End Data Pipeline Observability
Hi Fabric community, I've been exploring ways to enhance observability across our data pipelines in Fabric (especially Dataflows Gen2 and Lakehouses). With complex transformations and multiple data ...
Hi Anonymous ,
Thank you for reaching out to Microsoft Fabric Community.
Below documentations might help in understanding the required concepts :
- Column-level lineage visibility
To gain visibility into how data flows at the column level, you can use the Fabric Lineage Extractor. This tool helps trace data transformations across pipelines making it easier to audit and debug.- Automated Anomaly Detection
Built in Anomaly Detection adds "Find Anomalies" in line charts. It highlights outliers and provides natural language explanations. You can customize sensitivity and analysis fieldsMultivariate Anomaly Detection trains models using Spark notebooks and apply them in real-time via Eventhouse and KQL queries. This is ideal for detecting joint anomalies across correlated metrics- Proactive alert thresholds
Use Data Activator to trigger alerts based on conditions in streaming or batch data.Using Purview DLP Policies, Set up alerts for sensitive data access or schema changes- Track pipeline health beyond just activity logs
Use Dataflow Gen2 Optimizations for Fast Copy, query folding and staging Lakehouse/Warehouse to improve performance.Fabric’s Eventhouse based monitoring collects logs and metrics across items. You can query this using KQL or SQL for performance insights- Monitor sensitive data columns (PII/PHI)
Purview Hub Centralized dashboard helps to monitor sensitivity labels, data loss prevention (DLP) and audit logs.Use ai.extract and ai.generate_response to detect PII directly in pipelines. This is a native, LLM-powered alternative to external libraries like Presidio- Maintain data quality SLAs
To maintain data quality SLAs, you can schedule regular data profiling jobs in Fabric notebooks or Dataflows. Store results (row counts, null ratios etc) in a "data quality metrics" table in the Lakehouse/Warehouse. Power BI can then visualize SLAs and alerts can notify you when metrics breachsThank you !!
v-sathmakuri
Community Support
Community SupportHi Anonymous ,
I hope the information provided is helpful. Feel free to reach out if you have any further questions.
Thanks!!