TECHNICAL CASE STUDY

Automated ETL & Data Orchestration Pipeline

Enterprise DAG Orchestration with Apache Airflow & MinIO Storage

Executive Overview

An automated data engineering pipeline designed to ingest, clean, transform, and store unstructured and structured analytics datasets using Apache Airflow and MinIO object storage.

The Problem

Manual data extraction and raw file ingestion create bottlenecks, data inconsistencies, and silent failure points for downstream analytics applications.

My Engineering Role

Data Engineering Intern — Designed Apache Airflow DAGs, set up MinIO object buckets, created Dockerized task environments, and wrote ETL transformation scripts.

System Architecture Flow

[01]Data Source (APIs / Web Scraping) → Apache Airflow Ingestion Task

[02]Airflow Task → Raw Data Stored in MinIO Object Storage (S3-compatible)

[03]Airflow Transformation DAG → Python Transformation & Data Cleaning

[04]Cleaned Data → PostgreSQL / Warehouse Analytics Destination

Key Architectural Decisions

Apache Airflow for Task Orchestration

Provided programmatic Python DAG definitions, task dependency visualization, and built-in logging.

MinIO Object Storage

Delivered lightweight, S3-API compatible object storage ideal for local and cloud data staging.

Verified Outcomes & Results

✓Successfully created automated DAGs capable of hands-free daily data processing.
✓Established reliable error logging and workflow state management.

Lessons Learned

•Idempotency in ETL task design is essential—running a DAG task multiple times must produce identical, side-effect-free results.