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Fabric spark workflow

Fabric spark workflow. 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 change data feed (CDF), resolving delta table not found errors, or optimizing MLV query definitions for incremental refresh eligibility. Covers Spark SQL syntax, TBLPROPERTIES, PARTITIONED BY, CONSTRAINT CHECK ON MISMATCH DROP/FAIL, and custom environment configuration. Manages Fabric Spark work, including notebook cell code with %%configure, %%sql, PySpark and notebookutils, named notebook runs, Livy sessions, triage of.

Works with the context you provideVersion 1.0.1

Fabric spark workflow

Use this skill for the outcome in its description. Start with the supplied request and evidence, identify the specific task, and distinguish planning from execution. Do not trigger solely because the subject happens to be fashion, retail, or a technology named in an example. Read `references/execution-contract.md` before applying a retained source method.

Select the narrowest matching method below, then read its full entrypoint and the resources it requires. If multiple methods cover the same outcome, choose the one matching the user's environment and inputs; do not run them all. Retain each method's input requirements, decision steps, validation, output structure, and failure handling. If prerequisites are absent, use its honest proposal-only fallback or state that its documented path is blocked.

  • **PatrickGallucci/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 change data feed (CDF), resolving delta table not found errors, or optimizing MLV query definitions for incremental refresh eligibility. Covers Spark SQL syntax, TBLPROPERTIES, PARTITIONED BY, CONSTRAINT CHECK ON MISMATCH DROP/FAIL, and custom environment configuration. Read `references/PatrickGallucci/skills/fabric-lakehouse-views-perf-remediate/SKILL.md`.
  • **microsoft/spark-cli**: Manages Fabric Spark work, including notebook cell code with %%configure, %%sql, PySpark and notebookutils, named notebook runs, Livy sessions, triage of failed or OOM notebook and pipeline Spark runs, and the Materialized Lake View (MLV) lifecycle. Load it before writing MLV SQL, since CREATE MATERIALIZED LAKE VIEW and its CONSTRAINT clause are Fabric-only. KQL materialized views belong to eventhouse-cli. Read `references/microsoft/skills/spark-cli/SKILL.md`.

Return the requested deliverable with evidence, assumptions, validation performed, unresolved dependencies, and actual execution status. Source-specific results must be verified independently; importing these methods does not establish a working service connection.