Data Pipelines With Apache Airflow Udemy
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Data Pipelines with Apache Airflow
Author: Julian de Ruiter
language: en
Publisher: Simon and Schuster
Release Date: 2021-04-05
"An Airflow bible. Useful for all kinds of users, from novice to expert." - Rambabu Posa, Sai Aashika Consultancy Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs. What's inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Scheduling in Airflow 4 Templating tasks using the Airflow context 5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows 7 Communicating with external systems 8 Building custom components 9 Testing 10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices 12 Operating Airflow in production 13 Securing Airflow 14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds 16 Airflow on AWS 17 Airflow on Azure 18 Airflow in GCP
Data Engineering with Python
Author: Thompson Carter
language: en
Publisher: Independently Published
Release Date: 2024-11-08
"Data Engineering with Python: Build Efficient Data Pipelines with Python, SQL, and Airflow" Unlock the potential of your data with "Data Engineering with Python," the definitive guide for anyone looking to excel in data-driven decision-making. Whether you're an aspiring data engineer or a professional seeking to build robust data pipelines, this book offers you an in-depth journey through the essential aspects of data engineering. From data collection, storage, and transformation to building and optimizing pipelines using Python, SQL, and Apache Airflow, this book provides hands-on, real-world examples and best practices for success. Learn to manage big data, integrate machine learning into data workflows, and harness cloud platforms like AWS to scale your infrastructure. By the end, you'll have the tools to transform raw data into valuable insights, setting you up for a thriving career in one of today's fastest-growing fields.
Data Pipeline Automation Handbook
Author: Newman Chandler
language: en
Publisher: Independently Published
Release Date: 2025-07-03
Data Pipeline Automation Handbook: Expert Strategies for Scheduling, Monitoring, and Optimizing with Airflow & Mage Are you wrestling with fragile data workflows that falter under load? Do you long for bullet-proof pipelines that run on autopilot-scaling effortlessly and alerting you before issues arise? Data Pipeline Automation Handbook delivers expert strategies for building, scheduling, monitoring, and optimizing data pipelines with Apache Airflow and Mage. This no-nonsense guide shows you how to harness Airflow's enterprise-grade orchestration alongside Mage's modular, cache-aware blocks to create workflows that are both powerful and easy to maintain. Inside, you'll learn how to: Design reliable DAGs and event-driven Mage pipelines for any use case-batch ETL, real-time streaming, or machine-learning orchestration. Implement robust scheduling and sensor patterns in Airflow to trigger jobs on time and guard against missed runs. Configure Mage's runtime variables and webhooks for responsive, parameterized pipelines. Monitor performance with structured JSON logs, StatsD metrics, and integrations for Prometheus or Datadog-catching regressions before SLAs slip. Scale your infrastructure on Kubernetes, tune parallelism and pools, and leverage caching to eliminate unnecessary work. Secure your environment with Vault or Kubernetes Secrets, enforce RBAC, and automate credential rotation for compliance. Automate deployments via GitOps with Terraform and Helm, plus set up CI/CD pipelines that validate, test, and promote changes without downtime. By the final chapter, you'll wield a comprehensive playbook-covering custom plugins, AI-assisted pipeline generation, incident response checklists, and best practices for testing, versioning, and continuous delivery. Ready to transform your data engineering practice? Order Data Pipeline Automation Handbook now and build pipelines that deliver insights reliably, at scale, and with confidence.