scala-spark

Spark is easy to get running and hard to get right. I've spent years working specifically with Scala and Spark, so I know where the traps are and how to build things that actually hold up at scale. What I can help with: - Building analytics and ETL pipelines in Scala/Spark from scratch, or cleaning up ones that have gotten messy. - Making slow jobs fast, sorting out shuffles, joins, partitioning, and the memory issues that quietly kill performance.

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example2-output.scala

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Ignition SQL Query Builder without API

Created from 10+ years of manufacturing and industrial automation experience across food packaging, planter-part manufacturing, and Tier 1 automotive environments, with Ignition used in production-facing work over that span. The skill packages practical SCADA judgment around maintainable expressions, operator-safe behavior, troubleshooting habits, and realistic Designer/Gateway validation workflows.

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Full-Pipeline Data Analysis Agent

A full analysis pipeline that turns a dataset into a finished report, not just charts. Hand it a CSV, Excel, Parquet, or JSON file and it cleans the data, scores its quality, runs EDA, and where the data supports it, trains a prediction model with SHAP attribution and a backtested forecast. You get a trust score, ranked findings, and recommendations as a Word doc, an interactive HTML report, and an optional PowerPoint deck. The numbers come from code, so they stay consistent, never invented.

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scala-spark

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