Spark Consumption Cli
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".
30-Second Summary
Spark Consumption Cli is an agent-readable workflow from microsoft/skills-for-fabric for spark consumption cli.
It gives the agent a trigger, ordered guidance, and source-backed checks instead of treating spark consumption cli as a generic tool.
Use it as a brief only while the linked SKILL.md remains available and the risk boundary is still accurate.
1-Minute Read
What it is
Spark Consumption Cli packages instructions from microsoft/skills-for-fabric into a reusable agent skill brief. The original source is a public SKILL.md file, so the brief can point readers back to the executable workflow instead of a product landing page.
When to use it
Use it when a user asks for help around spark consumption cli and the agent needs a repeatable workflow, checklist, or review path rather than broad background information.
How to test it first
Open the linked SKILL.md, confirm the trigger and procedure still match spark consumption cli, list the risk boundary, then ask the agent to apply the smallest read-only step to a sample task.
Watch out
Do not present this as a generic app or platform. Keep the scope tied to microsoft/skills-for-fabric. Risk boundary: database, Fabric workspace, notebook, lakehouse, warehouse, or Power BI changes need credential and production-data approval; cloud resources, RBAC, credentials, tokens, and private keys must not be changed or exposed without a separate approval; Microsoft Fabric-specific workflow; do not describe it as a generic data engineering skill.