Everything you need to know about Airflow

Nowadays, more and more companies turn into open-source systems and platforms compared to the more traditional solutions. In this article we will examine the power of Airflow. Let’s try to understand first how Airflow works and what is its purpose.

What is Airflow?

Airflow, an open-source platform, serves as a powerful tool for managing complex workflows and data processing. It provides a streamlined way to schedule, monitor, and manage workflows. Its highly user-friendly interface is one of the main reasons many companies have adopted this tool. Moreover, capable of handling diverse tasks, Airflow simplifies the management of data pipelines, allowing for efficient automation and scheduling. In Airflow, data pipelines are created in Python code as Directed Acyclic Graphs (DAGs). Think of a DAG as a flowchart where each box (node) represents a specific job to be done. These jobs could be anything from data extraction to processing or analysis. The lines (edges) connecting the boxes show the order in which these jobs need to be done. So, each box (task) is like a piece of work, and the arrows between them show which tasks need to be finished before others can start.

How to use Airflow?

Apache Airflow can be used in a variety of different scenarios, including:

• ETL pipelines: Airflow can be used to automate the extraction, transformation, and loading (ETL) of data from one or more sources to a target destination. For example, Airflow could be used to extract data from a relational database, transform it into a format that is compatible with a data warehouse, and then load the data into the data warehouse.

• Machine learning pipelines: Airflow can be used to automate the training, deployment, and monitoring of machine learning models. For example, Airflow could be used to schedule the training of a machine learning model on a daily basis, deploy the model to a production environment, and monitor the performance of the model over time.

• Data science workflows: Airflow can be used to automate a variety of data science workflows, such as data cleaning, feature engineering, and model evaluation. For example, Airflow could be used to schedule the execution of a Python script that cleans and transforms data, trains a machine learning model on the transformed data, and then evaluates the performance of the model.

• DevOps tasks: Airflow can be used to automate a variety of DevOps tasks, such as code deployments, configuration management, and infrastructure provisioning. For example, Airflow could be used to schedule the deployment of a new version of a software application to a production environment, automate the configuration of a new server, or provision a new cloud instance.

What is DAG?

The abbreviation DAG stands for Directed Acyclic Graph, which is the core concept behind Airflow. In general, a DAG is a collection of tasks that you want to run and organize in a way that shows their relationships and dependencies, as we briefly described before.

Directed means that there is a specific order in which tasks need to be executed. Some tasks must run before others, which is a common case in a data warehouse.

Acyclic refers to the absence of cycles or loops within the structure.

In a general sense, think about it like a family tree. Your parents need to get together to create you, but you cannot create your parents. This is exactly how a DAG works.

Each workflow in Airflow represents a different DAG, with each DAG composed of operators, meaning individual tasks that need to be performed.

Airflow provides several features that make it easy to create and manage DAGs, including:

A built-in scheduler that can automatically execute DAGs at scheduled times A web interface allowing users to view and manage DAGs A Python API enabling users to interact with DAGs programmatically.

Fivetran or Airflow?

Naturally, you might wonder, "Which tool should I use to automate my process?" Well, the answer depends primarily on your infrastructure and secondarily on your final goal. If you need to swiftly and easily get data into your data warehouse, without requiring a significant level of control over the data integration process, then I would suggest Fivetran as the right choice for you.

On the other hand, if you necessitate control over your data integration and need to integrate data from various sources, then you should definitely opt for Airflow. The primary reason is that Airflow offers unlimited choices when it comes to sources. Additionally, keep in mind that Airflow also provides a free version of the tool, unlike Fivetran.

Airflow VS AWS Step Functions?

Apache Airflow and AWS Step Functions are both workflow management tools, but they have some key differences, whereas AWS Step Functions is a fully managed serverless workflow service. Both of these tools are easy to use. However, Airflow is highly customizable and can be used to automate a wide range of workflows, including data pipelines, machine learning workflows, and business processes. On the other hand, AWS Step Function can automate a variety of AWS services, such as Lambda, ECS, and Batch. Also the programming language these tools are using is different. Airflow uses Python and AWS Step Function uses visual workflow builder.

If you are looking for a workflow management platform that is flexible, scalable, reliable, and has a large community support, then Apache Airflow is a good choice.