Data Pipelines With Apache Airflow

Synopsis

Pipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science.

 

Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you're done you'll be set to start using Airflow for seamless data pipeline development and management.

 

Key Features

Framework foundation and best practices

Airflow's execution and dependency system

Testing Airflow DAGs

Running Airflow in production

 

For data-savvy developers, DevOps and data engineers, and system

administrators with intermediate Python skills.

 

About the technology

Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it's needed -- whether that's visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack.

 

Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

Read more

Summary

Chapter 1: Introduction to Data Pipelines and Airflow

* Summary: Introduces data pipelines, their benefits, and challenges. Presents Apache Airflow as a solution for building and managing data pipelines. Provides real-world examples of data pipelines in various industries.
* Example: An e-commerce application that uses an Airflow pipeline to track customer purchases, generate invoices, and send email notifications.

Chapter 2: Building Airflow Pipelines

* Summary: Covers the basics of building Airflow pipelines, including DAGs, Operators, and TaskFlow API. Provides guidance on writing Python scripts for tasks and using Airflow's rich library of Operators.
* Example: A pipeline that extracts data from a database, transforms it using Pandas, and loads it into a data warehouse using the BigQuery Operator.

Chapter 3: Scheduling and Monitoring Airflow Pipelines

* Summary: Explains how to schedule and monitor Airflow pipelines using various scheduling options (Cron expressions, trigger rules, etc.). Covers the use of Airflow's Web Server and the CLI for pipeline monitoring.
* Example: A pipeline that runs on a daily basis at midnight and notifies the team if it fails via email using the Email Operator.

Chapter 4: Orchestrating Complex Pipelines

* Summary: Demonstrates how to handle complex data pipelines that involve multiple DAGs, dependencies, and branching logic. Introduces concepts like SubDAGs, XComs, and Bash Operators for task execution.
* Example: A multi-layer pipeline that ingests data from multiple sources, performs data cleaning and enrichment, and finally loads the data into a production database.

Chapter 5: Error Handling and Recovery in Airflow

* Summary: Discusses error handling techniques in Airflow, including using Branch Operators, Retry Operators, and SLA (Service Level Agreement) monitors. Provides tips for designing pipelines that can handle failures and data inconsistencies.
* Example: A pipeline that implements an exponential backoff retry strategy to handle temporary service outages during data extraction.

Chapter 6: Airflow Extensions and Best Practices

* Summary: Introduces extensions and best practices for enhancing Airflow pipelines, such as using custom Operators, Plugins, Airflow Variables, and SLAs. Provides guidance on pipeline documentation, version control, and performance optimization.
* Example: A custom Operator that simplifies the process of sending data to a REST API endpoint, reducing code duplication and improving maintainability.