Seeking Best Practices for End-to-End Data Pipeline Observability

[zakariamekkaoui]

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:

1. Track pipeline health beyond just activity logs

2. Monitor sensitive data columns (PII/PHI)

3. 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!

[v-sathmakuri]

Hi @zakariamekkaoui ,

Could you let us know if the response provided was helpful in resolving your issue?

Thank you!!

[v-sathmakuri]

Hi @zakariamekkaoui ,

Could you please let us know whether the provided response helped in resolving the issue. 

Thank you!!

[v-sathmakuri]

Hi @zakariamekkaoui ,

I hope the information provided is helpful. Feel free to reach out if you have any further questions.

Thanks!!

[v-sathmakuri]

Hi @zakariamekkaoui ,

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.

https://github.com/sdetoni-prj/Fabric_LineageExtractor?tab=readme-ov-file 

• 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 fields

https://tip2.blog.fabric.microsoft.com/en-us/blog/anomaly-detection-preview?ft=Announcements:catego... 

Multivariate 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

https://learn.microsoft.com/en-us/fabric/real-time-intelligence/multivariate-anomaly-detection  

• 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

https://learn.microsoft.com/en-us/fabric/governance/use-microsoft-purview-hub?tabs=overview  

• Track pipeline health beyond just activity logs

Use Dataflow Gen2 Optimizations for Fast Copy, query folding and staging Lakehouse/Warehouse to improve performance. 

https://learn.microsoft.com/en-us/fabric/data-factory/dataflow-gen2-performance-best-practices  

Fabric’s Eventhouse based monitoring collects logs and metrics across items. You can query this using KQL or SQL for performance insights

https://learn.microsoft.com/en-us/fabric/fundamentals/workspace-monitoring-overview   

• Monitor sensitive data columns (PII/PHI)

Purview Hub Centralized dashboard helps to monitor sensitivity labels, data loss prevention (DLP) and audit logs.

https://learn.microsoft.com/en-us/fabric/governance/use-microsoft-purview-hub?tabs=overview  

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

https://community.fabric.microsoft.com/t5/Data-Engineering-Community-Blog/PII-Detection-and-Redacti... 

• 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 breachs

Thank you !!