Lead Data Prep Tools

700,000 records. Under 8 minutes.

The Problem

Data engineers and ops people watch Python scripts choke on 50,000 rows.

Encoding mismatches, messy headers, mixed formats, duplicates, and memory constraints cause failures at scale. When you’re processing hundreds of thousands of records, naive implementations don’t just slow down — they crash.

The Solution

Lead Data Prep Tools processes 700,000+ records in under 8 minutes through vectorized processing and memory-efficient chunking.

The Pipeline

11 configurable steps:

  1. Smart Read — Encoding detection (UTF-8, Latin-1, BOM handling)
  2. Header Profiling — Analyzes and standardizes column names
  3. Schema Mapping — Multi-tier matching (Exact → Alias → Fuzzy)
  4. Date Standardization — ISO-8601 conversion (YYYY-MM-DD)
  5. Address Parsing — Extracts Street/City/State/ZIP from raw text
  6. Data Quality Scoring — Grades every row (0-100) based on completeness
  7. Deduplication — Exact and fuzzy matching strategies
  8. Batch Splitting — Breaks large files into campaign-ready batches
  9. Data Merging — Aligns disparate CSVs to a single schema
  10. Safe Write — Never modifies source files; writes to output/
  11. Memory Chunking — Processes potentially infinite-sized files

The Performance

700,000 records. Under 8 minutes.

That’s not just fast — it’s reliable. The system doesn’t choke on edge cases. It handles encoding mismatches, messy headers, mixed formats, and duplicates without crashing.

The Architecture

Vectorized Processing: Pandas operations are vectorized for maximum throughput. No row-by-row iteration.

Memory-Efficient Chunking: Process potentially infinite files by reading in chunks. Memory usage stays constant regardless of file size.

Multi-Domain Schema Mapping: Auto-detects if data is “Leads,” “Crypto Trades,” or custom schemas. Multi-tier matching handles column name variations.

The Audience

This is for data engineers and ops people who’ve watched Python scripts choke on 50,000 rows. Who know that scale isn’t about bigger machines — it’s about better algorithms.

Anonymized

This project represents anonymized consulting work. The client name and specific use case are not disclosed. The performance and architecture are real — the identity is protected.

GitHub: LDLA

Built with Python and Rust. 700,000 records in under 8 minutes.