Tiered Computational Processing Pipeline

Overview

A staged methodology for processing raw data exports: CLI tools handle initial cleaning and validation, then Python frameworks perform deeper analysis. Each stage has explicit inputs, outputs, and validation gates. Raw data is never modified in place.

When to Use

• Processing a raw data export (CSV, JSON, or mixed) into structured analytical datasets
• Building a repeatable pipeline from messy source files to clean, enriched corpora
• Combining CLI tools (csvkit, jq, minet, exiftool) with Python analysis (pandas, spaCy, NLTK)
• Integrating LLM-based scoring or classification into a traditional analysis pipeline
• Need checkpoint/resume capability across long-running analytical workflows

When NOT to use:

• Single-file, single-pass transformations (just use pandas directly)
• Real-time streaming data (this is batch-oriented)
• When the data is already clean and structured for immediate analysis