Apache Airflow

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What is Apache Airflow?

Airflow is an open-source workflow management system designed to programmatically author, schedule, and monitor data pipelines and workflows. The open-source distribution is available through the Apache Software Foundation.

Airflow was originally created by Airbnb and was open sourced in June 2015. Airflow is written in Python and uses the Django web framework. The goal of the project was to enable greater productivity and better workflows for data engineers.

How Airflow Works – Build and Monitor Workflows

DAGs: Airflow enables you to manage your data pipelines by authoring and monitoring workflows as Directed Acyclic Graphs (DAGs) of tasks, which instantiates pipelines dynamically. DAGs are composed of operators, which are nodes in the graph that represent an individual task. Operators can be grouped together to form upstream tasks. Tasks are then grouped together to form DAGs. DAGs can be created from configuration files or other metadata.

Hooks and executors in the Airflow environment: Hooks are pieces of code that are invoked by operators to interact with databases, servers, and external services. Airflow is built using hooks to abstract information; Airflow operators generate tasks that become nodes in a DAG, and executors (usually Celery) run jobs remotely and handle message queuing.

Advantages of Airflow’s dynamic pipeline generation

  • Highly extensible and plays well with a variety of data processing tools and services.
  • Airflow’s proponents consider it to be distributed, scalable, flexible, and well-suited to handle the orchestration of complex business logic.
  • Its well-defined architecture allows for high availability and strong security controls.
  • Highly customizable and allows for intricate workflows.
  • Open source and under constant development by the community.

Drawbacks of Airflow pipelines

  • Apache Airflow is a batch-processing workflow tool, not a streaming data solution
  • No pipeline versioning, making it difficult to track changes over time
  • Requires experienced Python developers to get the most out of it
  • Pipelines are hand-coded, which can be burdensome to isolate and repair
  • There is a steep learning curve

>> Popular Airflow articles from our archives:

Apache Airflow – When to Use it, When to Avoid it: Learn how Airflow enables you to manage your data pipelines via Directed Acyclic Graphs. We cover the benefits of using Airflow, as well as some potential pain points to be aware of. We also explain how Upsolver simplifies building batch and streaming pipelines and automates data management on object storage services – including pipeline workflow management.

Workflow Management Review: Airflow vs. Luigi: This article is about Airflow and Luigi, two popular workflow management software options. It compares and contrasts the two, discusses their similarities and differences, and provides information on when each would be the best choice.

Managed Airflow Services

Amazon’s Managed Workflows for Apache Airflow (MWAA) is a cloud-based service that makes it easier to create and manage Airflow pipelines at scale. MWAA enables developers to create Airflow workflows in Python, while AWS manages the infrastructure aspects. It also offers auto-scaling and can integrate Airflow with AWS security services.

Cloud Composer is Google’s managed workflow orchestration service, based on open-source Apache Airflow. Similar to the AWS offering, it is operated in Python and enables users to author, schedule, and monitor workflows. Google highlights Cloud Composer’s ability to run pipelines across hybrid and multi-cloud environments, which is meant to reduce vendor lock-in.

Astro Runtime by Astronomer is a cloud-based solution designed to optimize Airflow pipelines. It features auto-scaling, instant in-place upgrades to the latest version of Apache Airflow, and reduced task utilization. Astro Runtime also provides more granular monitoring – resource use can be viewed at the task-level.

>> Looking for an Airflow Alternative? Try Upsolver

Airflow is a popular tool in the data engineering community, but is also notoriously difficult to master. Managed Airflow services remove the infrastructure burden, but not the intricate configuration and coding required to manage workflows within Airflow.

Upsolver SQLake offers an alternative that is not only fully-managed, but completely self-service for both data engineers and analytics users. Unlike Airflow, Upsolver is operated entirely through SQL and does not require you to manage pipelines in Python, build a DAG, or write transformation code in Spark or Flink.

If you’re ready to stop writing code for data pipeline automation, give Upsolver a spin (for free). It’s shockingly easy and fast.

Resources to Get Started with Airflow

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