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 sources, tracking data quality issues upstream feels like finding needles in haystacks!
One approach I'm testing involves implementing data contracts between ingestion and transformation layers. This helps catch schema drifts early, but I still struggle with:
Column-level lineage visibility
Automated anomaly detection
Proactive alert thresholds
Has anyone built a comprehensive monitoring solution within Fabric? I'd love to hear how you:
Track pipeline health beyond just activity logs
Monitor sensitive data columns (PII/PHI)
Maintain data quality SLAs
For inspiration, I've been researching solutions like Sifflet's data observability platform. Their approach to metadata-driven monitoring seems promising for Fabric environments. Curious if others have tried similar tools or built custom solutions using:
Fabric REST APIs
Purview integration
Power BI anomaly detection
Would appreciate any war stories or architecture diagrams!
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 !!
5 Replies
v-sathmakuriCommunity Support
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
Hi Anonymous ,
I hope the information provided is helpful. Feel free to reach out if you have any further questions.
Thanks!!
- v-sathmakuri
Hi Anonymous ,
Could you please let us know whether the provided response helped in resolving the issue.
Thank you!!
- v-sathmakuri
Hi Anonymous ,
Could you let us know if the response provided was helpful in resolving your issue?
Thank you!!
- v-sathmakuri
Hi Anonymous ,
Please let us know if you need any assistance from our end.
Thank you!!