About Apache Airflow

Apache Airflow is a platform to programmatically author, schedule and monitor workflows.

When workflows are defined as code, they become more maintainable, version-able, testable, and collaborative.

Use Airflow to author workflows and the orchestration of data pipelines as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

History

Apache Airflow is a project started at Airbnb in October 2014. It's a highly flexible tool that can be used to automate all sorts of processes, including ETL pipelines.

Airflow has been gaining popularity in the past couple of years and is listed as one of the most popular tools (based on StackShare statistics). A lot of companies are already using Airflow in production today.

What is a workflow?

A workflow is a description of what you want to achieve, while a DAG describes how to achieve it. It's the difference between "I need to get from New York to San Francisco" and "my flight leaves JFK on time and arrives at LAX after a stopover in Denver".

Apache Airflow is a tool to express and execute workflows as directed acyclic graphs (DAGs).

It has a nice UI where you can see all the DAGs, their status, how long they took to run, if they are currently running or failed, if there are any tasks which need to be run and much more.

The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Example of a workflow on Apache Airflow

Apache Airflow allows data stewards (usually a data analyst or engineer) to create workflows that help sort and interact with raw data. It transforms this raw data into bits of information that are understandable to people within the data organization, and external to. For example, a data analyst may set up a workflow that calculates sales earnings on a daily basis- this means that the workflow goes from taking the purchase on the customers end, loading this, then transforming it into a value that a table or database understands, and then, creates a chart or final product that demonstrates this final value.

Components of Apache Airflow Architecture

As defined by Qubole, the components of Apache Airflow Architecture are as follows:

• Dynamic: Airflow pipelines are configured as code (Python), allowing for dynamic pipeline generation. This allows for users to write code that instantiates pipelines dynamically.
• Extensible: Easily define your own operators and executors, and extend the library so it fits the level of abstraction that suits your environment.
• Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the Jinja templating engine.
• Scalable: Airflow has a modular architecture and uses a message queue to communicate with and orchestrate an arbitrary number of workers.

Learn more with Secoda

Connecting Apache Airflow with Secoda can bring several benefits for data engineers, including:

1. Data Discovery: Secoda's data discovery feature can scan through all the data assets within an organization, regardless of their location or format. By connecting Apache Airflow with Secoda, data engineers can automatically discover the data lineage and dependencies of the workflows and pipelines managed by Airflow.
2. Data Classification: With Secoda, data engineers can classify data based on sensitivity levels, compliance requirements, and business value. By integrating Airflow with Secoda, data engineers can ensure that the workflows and pipelines manage data classified correctly.
3. Data Lineage: Secoda's data lineage feature can help data engineers to understand the origin, transformation, and movement of data across various systems, applications, and processes. By connecting Airflow with Secoda, data engineers can visualize the data lineage of the workflows and pipelines and understand data dependencies.
4. Compliance: Secoda can help ensure that the workflows and pipelines managed by Airflow comply with regulatory requirements such as GDPR, CCPA, HIPAA, and others. By integrating Airflow with Secoda, data engineers can monitor compliance status and take appropriate actions if necessary.
5. Data Quality: Secoda can help data engineers to monitor data quality issues in the workflows and pipelines managed by Airflow. By connecting Airflow with Secoda, data engineers can track data quality metrics, set up alerts, and take corrective actions when necessary.

In summary, connecting Apache Airflow with Secoda can bring benefits such as data discovery, data classification, data lineage, compliance, and data quality. These features can help data engineers to improve the governance, security, and quality of the data pipelines and workflows managed by Airflow.

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