Most real-world data starts messy — inconsistent CSV exports from legacy systems, spreadsheets with merged cells, date formats that vary by row. A good data pipeline transforms this chaos into clean, normalized database records.
Pipeline Architecture
The classic ETL (Extract, Transform, Load) pattern remains the foundation. Each stage has a clear boundary and failure mode.
Extract → Read raw data from source (CSV, API, FTP)
Transform → Normalize, validate, enrich, deduplicate
Load → Write clean records to target database
Monitor → Log anomalies, send alerts on threshold breachesHandling Messy Data
- Date normalization: Parse multiple formats (MM/DD/YYYY, DD-MM-YYYY, ISO 8601) into a single canonical format
- Address standardization: Normalize street abbreviations (St → Street, Ave → Avenue) and validate against postal databases
- Deduplication: Use fuzzy matching (Levenshtein distance, Jaro-Winkler) to identify duplicate records with slight variations
- Type coercion: Convert "yes"/"no"/"1"/"0"/"true"/"false" to consistent booleans
AI-Assisted Normalization
For particularly messy data, LLMs can classify and normalize fields that rule-based systems struggle with — company name variations, free-text product descriptions, and ambiguous category labels.
async function normalizeWithAI(rawValue: string, field: string): Promise<string> {
const response = await llm.complete({
prompt: `Normalize this ${field} value into a standard format.
Input: "${rawValue}"
Output (just the normalized value, nothing else):`,
maxTokens: 50,
temperature: 0,
})
return response.trim()
}Validation Layer
Every record should pass through validation before loading. Reject invalid records into a quarantine table for manual review rather than silently dropping data.
Wrapping Up
Data pipelines are unglamorous but critical. The difference between a good pipeline and a bad one shows up in data quality — and data quality directly impacts every business decision downstream.