Diagnose and resolve Microsoft Fabric OneLake performance issues including slow queries, cold cache latency, small file problems, Delta table fragmentation, V-Order optimization, Spark throttling, capacity SKU sizing, and cross-region data access. Use when remediate OneLake read/write performance, lakehouse query slowness, Direct Lake fallback, table maintenance failures, Spark concurrency limits, warehouse cold starts, or optimizing Delta parquet file layouts. Supports PowerShell, T-SQL, and Spark SQL diagnostic workflows.
Diagnose and resolve Microsoft Fabric Dataflow Gen2 performance issues including slow refresh times, Fast Copy optimization, query folding failures, staging bottlenecks, gateway latency, incremental refresh tuning, capacity throttling, and data destination write performance. Use when troubleshooting dataflow refresh failures, optimizing Dataflow Gen2 execution time, debugging Power Query mashup engine performance, resolving staging Lakehouse or Warehouse compute issues, configuring Fast Copy connectors, fixing insufficient permissions errors, monitoring dataflow refresh history, or automating dataflow health checks via REST API and PowerShell.
Troubleshoot and optimize pandas performance in Microsoft Fabric Spark notebooks. Use when diagnosing slow pandas operations, toPandas() out-of-memory errors, pandas API on Spark (pyspark.pandas) bottlenecks, DataFrame conversion failures, collect() memory issues, driver memory exhaustion, notebook cell timeouts, or when optimizing pandas workloads for Fabric capacity. Covers pandas vs Spark DataFrame conversion, memory profiling, broadcast joins, shuffle tuning, resource profiles, and Native Execution Engine integration.
Diagnose and resolve Microsoft Fabric Data Agent performance issues including slow query generation, capacity throttling (HTTP 430), Spark session startup delays, KQL/SQL/DAX query timeouts, data source misconfiguration, example query validation failures, resource profile tuning, VOrder optimization, autotune settings, and Lakehouse table maintenance. Use when asked to troubleshoot Fabric Data Agent response times, fix agent query accuracy, debug Operations Agent playbook performance, resolve capacity SKU limits, optimize Spark compute for agents, or diagnose Data Agent data source connection issues.
Diagnose and resolve Microsoft Fabric Data Agent issues including tenant settings, data source configuration, query generation failures, cross-region capacity errors, XMLA endpoint setup, Power BI semantic model integration, lakehouse/warehouse/KQL connectivity, example query validation, publishing/sharing problems, and Azure AI Foundry integration. Use when asked to troubleshoot data agent, fix Fabric AI agent, debug NL2SQL/NL2DAX/NL2KQL, resolve Copilot tenant settings, or diagnose Fabric data agent errors.
Create, configure, and manage Microsoft Fabric Data Agents that enable natural language Q&A over lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Use when asked to build data agents, configure NL2SQL/NL2DAX/NL2KQL experiences, write agent instructions, create example queries, automate data agent provisioning via REST API or PowerShell, integrate Fabric data agents with Azure AI Foundry, or troubleshoot data agent configuration issues.
Diagnose and resolve Microsoft Fabric Data Factory pipeline performance issues. Use when pipelines are slow, copy activities timeout, dataflows stall, activities are stuck, throughput is low, capacity is throttled, or jobs queue indefinitely. Covers copy activity tuning (parallelCopies, DIU, ITO, partitioning), pipeline monitoring via Monitoring Hub and workspace monitoring, Spark job queueing, capacity SKU limits, error code resolution, and dataflow optimization. Keywords include Fabric pipeline slow, copy activity performance, Data Factory throttling, pipeline timeout, activity stuck, TooManyRequestsForCapacity, HTTP 430, pipeline troubleshoot, dataflow performance, copy parallelism, intelligent throughput optimization.
Troubleshoot and optimize Delta Lake and Apache Spark performance in Microsoft Fabric. Use when diagnosing slow Spark jobs, small file problems, data skew, shuffle bottlenecks, out-of-memory errors, V-Order tuning, OPTIMIZE/VACUUM operations, partition strategy, resource profile selection (writeHeavy, readHeavyForSpark, readHeavyForPBI), autotune configuration, Native Execution Engine, broadcast joins, AQE (Adaptive Query Execution), or when Spark notebooks or Spark Job Definitions run slower than expected in Fabric Lakehouse workloads.