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Ahraf Khatri, home

ETL & Data Pipeline

High-Volume ETL Pipeline

Designed and implemented a scalable ETL pipeline processing millions of records with data cleaning, validation, and normalization.

Published

The problem

Raw data arrived inconsistent, duplicated and incomplete, and volumes reached millions of records. The pipeline had to clean and standardize that data reliably at scale.

How I approached it

  1. Extract from the sources

    Pull raw records from every source into a single, well-defined entry point.

  2. Clean and deduplicate

    Fix formatting problems, drop duplicates and fill or flag gaps.

  3. Validate against rules

    Every record is checked against explicit rules, and failures are separated instead of silently loaded.

  4. Normalize and load

    Records are mapped to a consistent schema and delivered to the destination systems.

Simplified architecture

  1. Data sources
  2. Extract
  3. Clean & validate
  4. Normalize
  5. Load to your systems

A simplified view of the stages, not a full system diagram.

Outcomes

  • Millions of records processed through one scalable pipeline
  • Clean, validated and consistently formatted output
  • A pipeline that scales with data volume