Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines,
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/spark-engineer
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines,
📊 Stats
Stars10,696
Forks973
LanguagePython
LicenseMIT
📦 Ships with fullstack-dev-skills
</> SKILL.md
spark-engineer.SKILL.md
---name: spark-engineer
description: Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
license: MIT
metadata:
author: https://github.com/Jeffallan
version: "1.1.0"
domain: data-ml
triggers: Apache Spark, PySpark, Spark SQL, distributed computing, big data, DataFrame API, RDD, Spark Streaming, structured streaming, data partitioning, Spark performance, cluster computing, data processing pipeline
role: expert
scope: implementation
output-format: code
related-skills: python-pro, sql-pro, devops-engineer
---# Spark Engineer
Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications.
## Core Workflow
1. **Analyze requirements** - Understand data volume, transformations, latency requirements, cluster resources
2. **Design pipeline** - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities
3. **Implement** - Write Spark code with optimized transformations, appropriate caching, proper error handling
5. **Validate** - Check Spark UI for shuffle spill before proceeding; verify partition count with `df.rdd.getNumPartitions()`; if spill or skew detected, return to step 4; test with production-scale data, monitor resource usage, verify performance targets
