Individual skill
fabric-spark-perf-remediate
Diagnose and resolve Apache Spark performance issues in Microsoft Fabric. Use when asked to troubleshoot slow Spark notebooks, optimize Spark SQL queries, fix data skew or shuffle bottlenecks, tune spark.sql.shuffle.partitions or autoBroadcastJoinThreshold, configure resource profiles (writeHeavy, readHeavyForSpark, readHeavyForPBI), enable autotune, resolve HTTP 430 throttling errors, analyze Spark UI stages and executors, optimize Delta Lake writes with VOrder or Optimized Write, run table maintenance (bin-compaction, vacuum, Z-Order), fix small files problems, tune streaming throughput, or right-size Fabric Spark pools and capacity SKUs.
skills/fabric-spark-perf-remediate/SKILL.md
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- Last checked
- Nov 6, 2026
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- Repository last updated
- Jun 7, 2026
- License
- MIT
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- claude Repository topic
- claude-skills Repository topic
- copilot Repository topic
- copilot-skills Repository topic
- fabric Repository topic
- microsoft-fabric Repository topic
- data Derived category
- design Derived category
- data-databases Derived category
- design-interface Derived category
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LICENSE.txtdoc · 11,358 bytesSKILL.mdcore · 8,568 bytesSKILL.mdcore · 8,568 bytesSKILL.mdcore · 8,568 bytesSKILL.mdcore · 8,568 bytesSKILL.mdcore · 8,568 bytesSKILL.mdcore · 8,568 bytesreferences/delta-lake-optimization.mddoc · 10,594 bytesreferences/monitoring-and-diagnostics.mddoc · 9,611 bytesreferences/spark-configuration-tuning.mddoc · 12,154 bytesscripts/Get-FabricSparkDiagnostics.ps1runs · 11,696 bytestemplates/spark-perf-notebook.pyruns · 8,933 bytes
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# Microsoft Fabric Apache Spark Performance remediate
Systematic workflows for diagnosing, analyzing, and resolving Apache Spark performance problems in Microsoft Fabric Data Engineering and Data Science workloads.
## When to Use This Skill
Activate when encountering any of the following scenarios:
- Spark notebooks or jobs running slower than expected
- Capacity throttling errors (HTTP 430 / TooManyRequestsForCapacity)
- Data skew detected by Spark Advisor in notebook cells
- Excessive shuffle read/write in Spark UI stages
- Small files accumulation in Delta Lake tables
- Streaming ingestion throughput degradation