Skills publicadas
fabric-dataflows-perf-remediation
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 en
fabric-delta-spark-perf
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 j
fabric-data-agent-perf-remediate
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 Age
fabric-data-agent-remediate
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 F
fabric-data-factory-perf-remediate
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, ca
fabric-data-agent
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 w
fabric-rti-perf-remediate
Diagnose and resolve performance issues in Microsoft Fabric Real-Time Intelligence including Eventhouse, KQL databases, Eventstream, and ingestion pipelines. Use when asked to troubleshoot slow KQL queries, high Eventhouse CPU or memory, ingestion latency or failures, Eventstream throughput problems, capacity throttling (HTTP 430), cache policy tuning, materialized view lag, or Always-On configura
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 Spa
fabric-lakehouse-perf-remediate
Diagnose and resolve Microsoft Fabric Lakehouse performance issues including slow Spark queries, small file problems, Delta table fragmentation, V-Order configuration, table maintenance (OPTIMIZE, VACUUM, Z-Order), SQL analytics endpoint tuning, Direct Lake performance, resource profile selection, autotune configuration, capacity throttling, and streaming ingestion optimization. Use when asked to
fabric-lakehouse-views-perf-remediate
Troubleshoot Microsoft Fabric materialized lake views (MLV) performance issues including slow refresh, incremental refresh failures, full refresh fallback, Spark job failures, lineage execution errors, data quality constraint violations, and optimal refresh configuration. Use when diagnosing MLV refresh duration, monitoring MLV runs in Monitor Hub, analyzing Spark logs for MLV failures, enabling c
fabric-network-remediate
Diagnose and resolve Microsoft Fabric network performance issues including connectivity failures, latency, private endpoints, managed VNets, outbound access protection, gateway diagnostics, OneLake endpoint routing, service tag configuration, firewall allowlisting, DNS resolution, and Spark session startup delays. Use when remediate Fabric networking, slow Spark jobs, connection timeouts, private
fabric-onelake-perf-remediate
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,
fabric-pandas-perf-remediate
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 DataF
fabric-pbi-security-remediate
Diagnose and resolve Microsoft Fabric Power BI security issues including row-level security (RLS), object-level security (OLS), column-level security (CLS), workspace permissions, sensitivity labels, service principal authentication, XMLA endpoint access, DirectLake security fallback, Entra ID app registration, and data loss prevention (DLP) policy restrictions. Use when remediate access denied er
fabric-performance-monitoring
Monitor and optimize Microsoft Fabric capacity, Spark compute, and workload performance. Use when asked to check capacity utilization, diagnose throttling (HTTP 430), monitor Spark VCore consumption, analyze CU usage, review Monitoring Hub jobs, query Fabric REST APIs for capacity health, generate performance reports, tune Spark resource profiles, investigate concurrency limits, or optimize Fabric
fabric-rest-api-perf-remediate
Diagnose and resolve Microsoft Fabric REST API performance issues including HTTP 429 throttling, long running operation (LRO) timeouts, pagination bottlenecks, and slow API response times. Use when remediate Fabric API latency, capacity throttling, retry-after handling, operation polling failures, bulk API call optimization, or automating Fabric REST API diagnostics with PowerShell. Covers api.fab
fabric-pyspark-perf-remediate
Diagnose and resolve Apache Spark performance issues in Microsoft Fabric notebooks and Spark Job Definitions. Use when PySpark jobs are slow, notebooks take too long, Spark stages are skewed, shuffles are excessive, out-of-memory errors occur, Delta Lake writes are slow, or Fabric capacity is throttled. Covers data skew, shuffle optimization, broadcast joins, partition tuning, VOrder, Optimized Wr
fabric-rest-api-remediate
Diagnose and resolve Microsoft Fabric REST API errors including HTTP 401 Unauthorized, 403 Forbidden, 404 Not Found, 429 Throttling, and 5xx server errors. Use when debugging Fabric API authentication failures, Entra ID token issues, insufficient scopes, throttling/rate limiting, long running operation (LRO) polling failures, pagination problems, service principal configuration, or capacity API er
fabric-spark-compute-perf-remediate
Diagnose and resolve performance issues in Microsoft Fabric Delta Lake and Spark workloads. Use when remediate slow Spark notebooks, long-running Spark jobs, Delta table query degradation, small file problems, VOrder configuration, resource profile tuning, autotune configuration, capacity throttling (HTTP 430), session startup delays, executor memory errors, skewed partitions, or streaming ingesti
fabric-spark-compute-remediate
Diagnose and resolve Microsoft Fabric Spark compute issues including HTTP 430 throttling errors, job queueing failures, slow session startup, autoscale problems, capacity SKU limits, environment publishing failures, library conflicts, node sizing, burst factor configuration, and VNet provisioning delays. Use when remediate Spark notebooks, Spark job definitions, lakehouse operations, starter pools
fabric-iq
Guia para o Microsoft Fabric IQ (preview), uma carga de trabalho de inteligência semântica para dados unificados e vocabulário de negócios. Utilize-o para criar e gerenciar itens de ontologia, definir tipos de entidade e relacionamento, vincular dados, configurar agentes de dados e consultar grafos de ontologia no Microsoft Fabric.
fabric-lakehouse-access-control
Solucione problemas de controle de acesso do Microsoft Fabric Lakehouse, abrangendo funções de segurança do OneLake, permissões de endpoint de análise SQL, funções de workspace, funções de acesso a dados e vários recursos de segurança, como RLS, CLS, OLS e mascaramento dinâmico de dados.
fabric-notebook-perf-remediate
Diagnostica e resolve problemas de desempenho em notebooks do Microsoft Fabric com Apache Spark, abordando lentidão, timeouts, erros de throttling, distorção de dados, shuffles ineficientes, otimização de tabelas Delta Lake, configuração V-Order, alta utilização de capacidade, erros OOM e avisos do Spark Advisor.
fabric-pbi-perf-remediate
Diagnostica e resolve problemas de desempenho do Power BI no Microsoft Fabric. Esta skill aborda relatórios lentos, consultas DAX ineficientes, otimização de modelos semânticos, baixo desempenho do DirectQuery, limitação de capacidade, visuais lentos e problemas de atualização de dados, utilizando ferramentas como Performance Analyzer e DAX Studio.
fabric-udf-perf-remediate
Diagnostica e resolve problemas de desempenho em Funções de Dados do Usuário do Microsoft Fabric. Utilize quando as funções estiverem lentas, com timeout, retornando erros, consumindo capacidade excessiva ou apresentando latência de inicialização a frio, cobrindo desde timeouts de execução até otimização Python.
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