Data Pipelines with Apache Airflow

Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines.SummaryA 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 technologyData 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 bookData 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 insideBuild, test, and deploy Airflow pipelines as DAGsAutomate moving and transforming dataAnalyze historical datasets using backfillingDevelop custom componentsSet up Airflow in production environmentsAbout the readerFor DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills.About the authorBas 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 ContentsPART 1 – GETTING STARTED1 Meet Apache Airflow2 Anatomy of an Airflow DAG3 Scheduling in Airflow4 Templating tasks using the Airflow context5 Defining dependencies between tasksPART 2 – BEYOND THE BASICS6 Triggering workflows7 Communicating with external systems8 Building custom components9 Testing10 Running tasks in containersPART 3 – AIRFLOW IN PRACTICE11 Best practices12 Operating Airflow in production13 Securing Airflow14 Project: Finding the fastest way to get around NYCPART 4 – IN THE CLOUDS15 Airflow in the clouds16 Airflow on AWS17 Airflow on Azure18 Airflow in GCP