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spark-consumption-cli

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التفرعات١٨٥

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Analyze lakehouse data interactively using Fabric Lakehouse Livy API sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".

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المصدر

microsoft

microsoft/skills-for-fabric

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تنزيل

تشغيل في Manus

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مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

SKILL.md

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spark-consumption-cli

Installation

# Copy this command into Claude Code to install this skill
/plugin add https://github.com/microsoft/skills-for-fabric/tree/main/skills/spark-consumption-cli

About

Author: microsoft
Stars: 678

For more information, visit the GitHub repository.

المزيد من هذا المستودع

نفس المستودع

mlv-operations-cli

microsoft/skills-for-fabric

Manage Microsoft Fabric Materialized Lake View (MLV) refresh schedules and job execution via REST APIs. Create, update, and delete refresh schedules (interval-based: hourly, daily, weekly). Trigger on-demand refreshes, monitor job status, and cancel running jobs. Uses human-in-the-loop confirmations for safety. Materialized Lake Views are also known as Spark Materialized Views, MLVs, or lakehouse materialized views in Fabric documentation. Note: MLV discovery (list MLVs, lineage, data quality) requires UI as REST APIs are not yet available. Triggers: "schedule MLV refresh", "manage MLV", "MLV refresh schedule", "schedule materialized lake view", "schedule materialized view", "automate MLV refresh", "trigger MLV refresh", "monitor MLV refresh", "MLV job status", "cancel MLV refresh", "refresh schedule", "MLV automation", "manage materialized lake view", "manage materialized view", "materialized view refresh", "spark materialized view schedule", "lakehouse materialized view", "refresh my materialized views"

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e2e-medallion-architecture

microsoft/skills-for-fabric

Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".

2026-06-25678

fabriciq-ontology-authoring-cli

microsoft/skills-for-fabric

Create and evolve Fabric IQ Ontology (preview) items from CLI — define entity types, properties (including timeseries), relationship types, and bind them to OneLake lakehouse tables (static + timeseries) or Eventhouse / KQL database tables (timeseries only). Uses the Fabric item-definition REST API (Create Item / Update Item Definition) with `InlineBase64` parts. Use to create a Fabric Ontology item; add or alter entity types, properties, or keys; add timeseries properties and bindings; bind an entity type to a lakehouse or Eventhouse table; add relationship types and contextualizations; or script ontology deployment from source. Triggers: "create fabric ontology", "add ontology entity type", "bind entity type to lakehouse", "bind entity type to eventhouse", "ontology timeseries binding", "add ontology relationship type", "ontology contextualization", "fabric iq ontology authoring", "update ontology definition"

2026-06-25678

spark-authoring-cli

microsoft/skills-for-fabric

Develop Microsoft Fabric Spark/data engineering workflows and write code in Fabric Notebook cells with intelligent routing to specialized resources. Provides workspace/lakehouse management, notebook code authoring (PySpark, Scala, SparkR, SQL), and Materialized Lake View (MLV) authoring (Spark SQL MLVs support incremental refresh; PySpark is full-refresh only). Routes to data engineering patterns, development workflow, or infrastructure orchestration. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "Delta Lake patterns", "Spark development", "lakehouse configuration", "write notebook code", "notebookutils", "notebook cell", "PySpark notebook", "%%sql cell", "%%configure", "fabric notebook", "run notebook", "notebook deployment", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh", "review MLV for incremental refresh", "MLV refresh policy", "infrastructure provisioning"

2026-06-25678

fabriciq-ontology-consumption-cli

microsoft/skills-for-fabric

Explore Fabric IQ Ontology (preview) items (read-only) from the CLI to ground an agent before it queries data. Explore, describe, and summarize what an ontology exposes — its entity types, keys, relationships, and the bindings that map each concept onto a lakehouse or Eventhouse source — then route the underlying data query to the matching per-datasource consumption skill (eventhouse-consumption-cli, spark-consumption-cli, sqldw-consumption-cli). Read-only discovery via Get Item Definition; never writes to or alters an ontology. Use to explore or summarize an ontology, describe its schema and data lineage, build agent grounding context, or run an ontology-backed query over the source records. Triggers: "query fabric ontology", "explore fabric ontology", "list ontology entities", "enumerate ontology entity types", "describe ontology", "ontology grounding context", "ground query with ontology", "query ontology entity data", "fabric iq ontology consumption", "ontology-backed query", "ontology entity bindings"

2026-06-25678

spark-operations-cli

microsoft/skills-for-fabric

Diagnose failed Spark jobs, unhealthy Livy sessions, and performance bottlenecks in Microsoft Fabric via read-only CLI triage. Use when the user wants to: (1) diagnose why a Spark job, notebook run, or Lakehouse job failed, (2) triage stuck or dead Livy sessions, (3) identify OOM, shuffle spill, or data skew, (4) retrieve driver and executor logs or Spark Advisor findings, (5) copy event logs and start a local Spark History Server, (6) diagnose all Spark activities within a failed pipeline run. Triggers: "diagnose my failed notebook", "why did my spark job fail", "triage spark failure", "diagnose pipeline run failure", "why did my pipeline fail", "livy session stuck in starting", "spark executor OOM", "check spark advisor findings", "shuffle spill diagnosis", "why did my lakehouse job fail", "diagnose lakehouse table load", "data skew diagnosis", "open spark history server locally", "analyze spark failure logs", "spark job triage".

2026-06-25678

spark-consumption-cli

Installation

# Copy this command into Claude Code to install this skill
/plugin add https://github.com/microsoft/skills-for-fabric/tree/main/skills/spark-consumption-cli

About

Author: microsoft
Stars: 678

For more information, visit the GitHub repository.