fabric-delta-spark-perflisted

# Microsoft Fabric Delta Lake Spark Performance remediate Systematic workflows for diagnosing and resolving Apache Spark and Delta Lake performance issues in Microsoft Fabric Lakehouse environments. ## When to Use This Skill Activate when the user mentions any of the following: - Spark job is slow, taking too long, or timing out - Small file problem, too many small files, file fragmentation - Data skew, straggler tasks, unbalanced partitions - Out of memory (OOM) errors on driver or executor - Shuffle spill, excessive shuffle read/write - OPTIMIZE, VACUUM, bin-compaction, or table maintenance - V-Order, Z-Order, or Parquet optimization - Resource profiles: writeHeavy, readHeavyForSpark, readHeavyForPBI - Autotune, Adaptive Query Execution (AQE), broadcast join thresholds - Native Execution Engine configuration - Streaming performance, microbatch tuning, checkpoint issues - Spark pool sizing, autoscale, dynamic executor allocation - Direct Lake performance tied to Delta table structure - Capacity throttling, TooManyRequestsForCapacity errors ## Prerequisites - Microsoft Fabric workspace with Data Engineering or Data Science experience - Apache Spark notebooks or Spark Job Definitions - Lakehouse with Delta tables - Appropriate Fabric capacity SKU (F2 through F2048) ## Quick Diagnostic Workflow When a user reports slow Spark performance, follow this triage sequence: ### Step 1: Identify the Symptom Category | Symptom | Likely Root Cause | Jump To | |---------|--------