demo_airflow_etl

A small demo project showcasing how to use airflow for ETL pipelines

Table of Contents

• Demo Project: Airflow ETL Pipeline
• ETL: Extract, Transform, Load – Overview
  • Extract
  • Transform
  • Load
• Common Tech Stack
  • Extract
  • Transform
  • Load
  • Orchestration & Scheduling
  • Data Formats
  • Credentials & Secrets
  • Monitoring / CI/CD
• Further Reading

Project Overview

This project is designed to demonstrate the capabilities of Apache Airflow in managing ETL (Extract, Transform, Load) pipelines. It provides a practical example of how to set up and orchestrate data workflows, making it easier to understand the ETL process and the tools involved.

Project Structure

.
├── airflow  -> Contains Airflow DAGs and configurations
├── backend  -> Backend services for data retrieval and processing
├── docs  -> Documentation for the project
├── jobs  -> Contains job definitions and configurations
├── LICENSE  -> License file for the project
├── README.md  -> Project overview and documentation
├── requirements.txt  -> Python dependencies for the project
├── setup.py  -> Setup script for the project
└── utils  -> Utility functions and scripts

Demo Project: Airflow ETL Pipeline

This project demonstrates how to set up an ETL pipeline using Apache Airflow. The pipeline extracts data from various sources, transforms it into a clean and standardized format, and loads it into a target system for analysis.

ETL: Extract, Transform, Load – Overview

Extract

• Goal: Pull raw data from various sources (e.g., databases, flat files, APIs).
• Common Sources: CRM systems, relational databases, JSON/CSV files, cloud data services.
• Why a Staging Area? To buffer and preserve raw data before transformation in case errors occur.
• Format Handling: Compatible with structured (SQL) and unstructured formats (JSON, APIs, etc.).

Transform

• Goal: Clean and standardize data into a unified schema.
• Key Tasks:
  • Cleaning: Fix nulls, correct inconsistencies, remove duplicates and unnecessary attributes
  • Auditing: Ensure accuracy, completeness, and consistency.
  • Compliance: Protect sensitive data to meet laws (e.g., GDPR, HIPAA).
  • Formatting: Join tables, reformat columns, standardize types (e.g., date/currency).
    • Normalization:
      • Convert data types (e.g., string to integer, date formats)
      • Standardize units (e.g., convert all distances to meters)
      • String manipulations
        • Trimming whitespace, removing special characters
        • Converting to lowercase/uppercase
      • Replace code with text
        • E.g., replace country code with country name
      • Further normalization for consistency
        • Look-up tables, dictionaries to find valid data, synonyms, abbreviations
        • Normalize spellings of names, addresses, etc.
    • Building keys:
      • Combine data from multiple sources with common keys
      • Table matches production keys to surrogate DW keys
      • Correct handling of history - especially for total reload

Load

• Goal: Move cleaned, transformed data to a target system (data warehouse/lake).

• Types:

  • Initial Load: Full data migration.
  • Incremental Load: Only changes/new data.

• Scheduling: Typically automated via batch jobs or streaming systems.

• Outcome: Central, analysis-ready data repository for dashboards, analytics, or ML.

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Common Tech Stack

The Tech stack used in this demo project is bolded below, but there are many alternatives available for each task.

Extract

Task	Tools
APIs, Files, Databases	**Python**, **Requests**, **pandas**, **BeautifulSoup**, Selenium
Relational DBs	SQLAlchemy, psycopg2, pyodbc
NoSQL / Cloud	MongoDB Connector, **boto3** (for S3), Google Cloud Storage SDK
Stream Data	Kafka, Flink, Apache NiFi, Spark Structured Streaming
Workflow Triggers	**Apache Airflow**, Dagster, Prefect

Transform

Task	Tools
Batch Transformation	**PySpark**, **pandas**, dbt, SQL
Large-scale Transform	**Apache Spark**, Apache Beam, Dask, Flink
Geospatial Data	**GeoPandas**, **Shapely**, **PostGIS**, Osmnx
Cleaning + QA	Great Expectations, pandera, pyspark.sql.functions
Business Logic Layer	dbt (data build tool, for analytics-ready models)

Load

Target	Tools
Data Lakes	Amazon S3, Delta Lake, Apache Hudi, Iceberg
Data Warehouses	Snowflake, Google BigQuery, Redshift, **PostgreSQL**, Azure Synapse
Real-Time Store	**Redis**, ClickHouse, Elasticsearch
Geospatial Store	**PostGIS**, **GeoParquet**, Tile38

Orchestration & Scheduling

Tool	Description
Airflow	Popular for DAG-based ETL scheduling and task dependencies
Dagster	Modern orchestration with strong typing and observability
Prefect	Lightweight alternative to Airflow, Python-native
Luigi	Simpler pipelines with dependency chaining

Data Formats

Format	Use Case
Parquet	Columnar, efficient for large-scale processing
CSV	Simple, but not optimized for performance
JSON	Good for semi-structured data or API responses
Avro / ORC	Often used with Kafka or Hadoop ecosystems

Credentials & Secrets

Tool	Use Case
AWS Secrets Manager / GCP Secret Manager	Managed secrets
.env + dotenv	Local development or simple deployments
Vault (HashiCorp)	Enterprise-grade secret management

Monitoring / CI/CD

Tool	Description
GitHub Actions / GitLab CI	CI/CD automation pipelines
Prometheus + Grafana	Metrics + dashboards for pipeline health
Sentry, PagerDuty	Alerting & error logging

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Further Reading

1. ETL PPT
2. Airflow Documentation