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Data EngineeringCompleted

ETL Log Processing Pipeline

High-performance ETL pipeline processing 1.8M+ server logs with PostgreSQL, Airflow, and batch optimization.

Screenshot 1

Tech Stack

PythonPostgreSQLAirflowBashCron
Problem
Large-scale unstructured logs (~1.8M records) required structured transformation and reliable ingestion.
Solution
Built modular ETL pipeline with Bash extraction, Python transformation, and PostgreSQL staging + upsert design.
Architecture
  • Raw log ingestion (.gz files)
  • Bash + AWK parsing layer
  • Python transformation engine
  • PostgreSQL staging tables
  • Airflow + Cron orchestration
Results
  • Processed 1.8M+ log records efficiently
  • Sub-minute bulk loading via PostgreSQL COPY
  • Idempotent pipeline design (no duplicates)
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