João Blasques (Jonas) / Orchestrating Data Pipelines with Apache Airflow: A Comprehensive Guide

Created Sat, 21 Jun 2025 09:30:00 +0100
475 Words
data engineering airflow orchestration tutorial docker gcp kubernetes bigquery data warehouse

Project Overview

This repository serves as a practical guide to building and orchestrating robust data pipelines using Apache Airflow. It covers essential concepts from basic workflow management to advanced deployments with Google Cloud Platform (GCP) and Kubernetes.

Key Concepts

  • Workflow Orchestration: Automating and managing complex data workflows with dependencies, scheduling, retries, and monitoring using Apache Airflow.
  • DAGs (Directed Acyclic Graphs): The core abstraction in Airflow for defining task dependencies, execution order, and workflow logic.
  • Extensible Operators & Integrations: Leveraging Airflow’s wide range of built-in operators and custom plugins to interact with databases, cloud services (GCP, Kubernetes), and external systems.
  • Scalable Deployments: Running Airflow locally for prototyping, or deploying on cloud and Kubernetes for production-scale, resilient, and distributed data pipeline execution.

Understanding Workflow Orchestration

At its core, this project emphasizes the importance of workflow orchestration for managing complex data tasks. It highlights the limitations of monolithic scripts and introduces the concept of Directed Acyclic Graphs (DAGs) as the foundation for defining task dependencies, retries, logging, and scheduling within Airflow. The guide differentiates between Data Lakes and Data Warehouses, and ETL vs. ELT processes, providing a foundational understanding for data pipeline design.

Airflow Fundamentals and Architecture

The repository delves into Airflow’s architecture, explaining key components such as the scheduler, webserver, DAG files, and metadata database. It illustrates how DAGs are declared, loaded, and executed, and details the different types of tasks (Operators, Sensors, TaskFlow) and executors (Local, Remote, Containerized) available in Airflow.

Data Ingestion to Local PostgreSQL

A practical example demonstrates ingesting NYC taxi trip data into a local PostgreSQL database. This section walks through setting up a PostgreSQL container, configuring network connectivity with Airflow, and creating a Python-based DAG to download, unzip, and insert data in chunks, showcasing dynamic variable usage for file and table naming.

Data Ingestion to Google Cloud Platform (GCP)

The project extends to cloud-based data ingestion, focusing on GCP. It outlines a multi-step pipeline that:

  • Downloads and unzips data files.
  • Converts data to Parquet format.
  • Uploads Parquet files to Google Cloud Storage (GCS).
  • Creates and manages external, temporary, and final tables in Google BigQuery, including data merging and deduplication. This section also covers setting up GCP connections within Airflow and managing BigQuery schemas.

Airflow with Kubernetes

For scalable and resilient deployments, the guide explores integrating Airflow with Kubernetes. It provides instructions on:

  • Enabling the Kubernetes Engine API.
  • Installing necessary tools like kubectl and Helm.
  • Creating a GKE cluster and Kubernetes namespaces.
  • Deploying Airflow using Helm charts, customizing values.yaml for specific configurations (e.g., LocalExecutor, package dependencies).
  • Accessing the Airflow web interface and deploying DAGs to the Kubernetes cluster.

Conclusion

This repository offers a hands-on approach to mastering data pipeline orchestration with Apache Airflow. By covering local setups, cloud integrations with GCP, and scalable deployments on Kubernetes, it provides a comprehensive resource for data engineers looking to build efficient, robust, and maintainable data workflows.