Performance Optimization for Notebook Execution in Microsoft Fabric Data Pipelines

Introduction

Microsoft Fabric has emerged as a comprehensive platform not only for PowerBI and Data Analytics but also for building robust end-to-end data ingestion pipelines. The platform offers various capabilities including Data Pipelines and Spark Notebooks to construct sophisticated data workflows.

Most enterprise data pipelines incorporate Spark Notebook code for:

• Data ingestion using dynamic metadata-driven architecture
• Transformation processes for individual artifacts like Dimensions and Facts
• Complex data processing workflows

However, many data engineers encounter a common performance challenge: notebooks that execute quickly in interactive development sessions become significantly slower when deployed in Data Pipeline activities. This article shares practical insights and proven solutions to optimize notebook execution performance in Microsoft Fabric Data Pipelines.

The Performance Challenge

The Problem: Notebooks running in interactive sessions (development mode) execute faster than the same notebooks when integrated into Data Pipeline activities.

The Impact: This performance degradation can significantly affect:

• Overall pipeline execution time
• Resource utilization efficiency
• Operational costs
• Data delivery SLAs

Let’s explore four common scenarios with their root causes and practical solutions.

Test Environment Setup

For this analysis, I used two simple test notebooks:

• Notebook1 and Notebook2: Basic notebooks that print current timestamps
• Execution time in interactive mode: Milliseconds
• Purpose: Isolate performance issues from complex business logic

Scenario 1: Single Pipeline, Single Notebook Activity

Configuration

• Total Execution Time: ~41 seconds
• Spark Session Startup: ~20 seconds
• Notebook1 Activity: ~21 seconds

🔍 Issue Analysis

The notebook takes significantly longer to execute compared to interactive mode.

💡 Root Cause

When executing a Data Pipeline, Microsoft Fabric needs time to:

1. Provision compute resources
2. Initialize the Spark session
3. Load necessary libraries and configurations

In interactive mode, the Spark session is already running and warm.

⚡ Solution

This is the baseline scenario with limited optimization opportunities. The Spark session startup time is unavoidable for the first execution.

Key Takeaway: Accept this as the baseline performance cost for pipeline execution.

Scenario 2: Single Pipeline, Multiple Notebook Activities

Configuration

Before Optimization:

• Total Execution Time: ~62 seconds
• Spark Session Startup: ~20 seconds (×2)
• Notebook1 Activity: ~21 seconds
• Notebook2 Activity: ~21 seconds

🔍 Issue Analysis

The pipeline creates a new Spark session for each notebook activity instead of reusing the existing session.

💡 Root Causes

1. High concurrency setting disabled: The “pipeline running multiple notebooks” feature is not enabled
2. Different Default Lakehouses: Notebooks have different default lakehouse configurations, forcing separate sessions

⚡ Solutions

Solution 1: Enable High Concurrency

Navigate to your Data Pipeline settings and enable the high concurrency option for running multiple notebooks.

Solution 2: Standardize Default Lakehouses

Ensure all notebooks in your pipeline use the same Default Lakehouse:

1. Open each notebook
2. Navigate to lakehouse settings
3. Set the same default lakehouse for all notebooks

📊 Performance Improvement

After Optimization:

• Total Execution Time: ~42 seconds
• Performance Gain: ~20 seconds (32% improvement)
• Spark Session Reuse: ✅ Achieved

Scenario 3: Multiple Pipelines with Nested Notebook Activities

Configuration

Common Architecture:

• PL-MainPipeline: Orchestrates the entire workflow
• PL-Pipeline1: Executes Notebook1
• PL-Pipeline2: Executes Notebook2

Performance:

• Total Execution Time: ~62 seconds
• Issue: Spark session not reused across different pipeline contexts

🔍 Issue Analysis

Even with proper configuration from Scenario 2, execution time reverts to ~62 seconds because sub-pipelines operate in different execution contexts.

💡 Root Cause

Multiple sub-pipelines cannot directly share Spark sessions due to isolation boundaries between different pipeline execution contexts.

⚡ Solution: Session Tags

Implement session tags to enable Spark session sharing across pipeline boundaries:

1. For each Notebook Activity:

• Navigate to: Notebook Activity → Settings → Advanced Settings → Session Tag
• Set a consistent session tag (e.g., “SharedComputation”)

1. Apply consistently: Use the same session tag across ALL notebook activities in ALL pipelines

🎯 Result

This configuration enables Data Pipelines to recognize and reuse existing Spark sessions, eliminating redundant session creation.

Scenario 4: Custom Spark Environments with Public Libraries

Configuration Challenge

Using custom Spark environments with public libraries that aren’t pre-installed.

🔍 Issue Analysis

Performance Impact Example:

• Standard session startup: 40–50 seconds
• With custom libraries: 4–5 minutes
• Root cause: Library installation during session initialization

💡 Strategic Approach

When to Use Custom Environments:

• ✅ Frequently used libraries across multiple notebooks
• ✅ Libraries required for most pipeline executions
• ✅ Consistent library versions needed

When to Use Inline Installation:

• ✅ Libraries used in only 1–2 notebooks
• ✅ Infrequently executed notebooks
• ✅ Experimental or temporary library needs

Best Practices Summary

🎯 Configuration Checklist

• Enable high concurrency for multiple notebook execution
• Standardize default lakehouses across all notebooks
• Implement consistent session tags for cross-pipeline sharing
• Optimize library installation strategy

📋 Monitoring & Validation

• Measure baseline performance before optimization
• Monitor Spark session reuse in pipeline logs
• Track execution time improvements
• Validate cost reduction metrics

🔧 Troubleshooting Tips

1. Session not reusing: Check lakehouse consistency
2. Cross-pipeline issues: Verify session tag implementation
3. Library conflicts: Review custom environment configuration
4. Performance regression: Audit recent configuration changes

Conclusion

Optimizing notebook execution in Microsoft Fabric Data Pipelines requires a systematic approach to session management and resource utilization. The strategies outlined in this article can deliver significant performance improvements:

• Immediate impact: 20–30% improvement with basic optimizations
• Advanced optimization: 65–70% improvement with comprehensive implementation
• Cost benefits: Reduced compute resource consumption and faster data delivery

The key is understanding how Spark sessions are created, managed, and shared across different pipeline contexts. By implementing these optimization strategies, data engineering teams can build more efficient, cost-effective data pipelines while maintaining reliability and scalability.

About This Analysis

All execution times are based on sample notebooks printing timestamps. Results were validated across multiple configurations and production environments. Individual results may vary based on specific use cases, data volumes, and infrastructure configurations.

Connect with me: arif.ansari@aewee.com, arif@zekibi.com
Consulting Services: Data Analytics & Engineering Solutions
LinkedIn: https://www.linkedin.com/in/arifhusen-ansari-8636b762
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