Data plays a crucial role in improving and managing the operations of any organization. However, adopting data-centric processes can often be challenging, as it requires coordinating every task across different heterogeneous systems and integrating them for the deployment of the product. To address such challenges, in 2014, the engineers at Airbnb developed Airflow, an open-source framework that enabled them to write and schedule workflows. And, with the help of the built-in web interface, allowed them to monitor the workflow. After the success of this project, it was adopted under the Apache Software Foundation first as an incubator project in the year 2016 and later as a top-level project in the year 2019.
In this blog, we aim to provide a comprehensive introduction to Airflow that covers everything from installation to its use cases.
Table of Contents
- Understanding of automation.
Understanding Apache Airflow
Apache Airflow is an open-source, workflow management platform developed for scheduling and monitoring data pipelines. It is programmed in Python, and the workflows are created through Python scripts. Besides, it was created under the principle of the “configuration as code” workflow platform. The use of Python language helps developers import the libraries and classes to aid them in creating the workflow.
Key Features of Airflow
- Easy to Use: A data pipeline can be deployed by anybody who is familiar with the Python programming language. It allows users to build any machine learning models, manage infrastructure, transfer data, and has no restrictions on the scope of the pipelines.
- Pure Python: It allows users to create data pipelines using standard Python features, including the data time formats for scheduling and the loops to generate tasks dynamically. This helps the users to build the data pipelines as flexibly as possible.
- Useful UI: Through a robust and modern web application, it enables users to monitor, schedule, and manage the data pipelines. Since it always provides total visibility into the status and logs of completed and ongoing tasks at all times, there is no need to understand the interface style.
- Robust Integrations: Many operators are available by Airflow and are ready to perform the task on the Google cloud platform, Amazon Web Services, and a variety of other third-party services. As a result, this makes it easy to implement into existing infrastructure and expand into next-generation technologies.
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Introducing Apache Airflow in Python Environment
- Step 1: Install it from PyPI using pip as follows:
pip install apache-airflow
- Step 2: Initialize the database as follows:
- Step 3: Start the Web Server, the default port is 8080. You can use the following code snippet for the same:
airflow webserver -p 8080
- Step 4: Start the scheduler to finish this step as follows:
The apache-airflow PyPI is a basic package that only installs what is needed to get started.
However, it performs some conditional imports of operators that might require some extra dependencies. As a result, sub-packages are installed depending on the users’ requirements.
Following is the list of a few subpackages available:
|all||pip install apache-airflow[all]|
|s3||pip install apache-airflow[s3]|
|gcp_api||pip install apache-airflow[gcp_api]|
|mysql||pip install apache-airflow[mysql]|
|postgres||pip install apache-airflow[postgres]|
|hdfs ||pip install apache-airflow[hdfs]|
|slack||pip install apache-airflow[slack]|
|hive||pip install apache-airflow[hive]|
|password||pip install apache-airflow[password]|
|rabbitmq||pip install apache-airflow[rabbitmq]|
Architecture and Core Concepts
Apache Airflow enables users to build and run workflows. A workflow is defined as DAG. The following section will shed some light on the working principle of DAGs.
DAG is a Directed Acyclic Graph, a series of tasks the user wishes to perform in the Airflow. It specifies dependencies between the tasks and also the order in which to execute and run them.
This type of graph is known as a directed acyclic graph (DAG) because it has directed edges and no loops or cycles. This acyclic characteristic is important since it protects us from running into circular dependencies (as seen in the diagram above, where task 2 depends on task 3 and vice versa). When attempting to execute the graph, these circular dependencies become troublesome since task 2 can only execute after task 3 has been finished, and task 3 can only execute once task 2 has been completed. This logical inconsistency causes a deadlock in which neither task 2 nor 3 can run, preventing the graph from being executed.
Due to circular dependencies, DAG cycles inhibit task execution. There is a clear method to accomplish the three different jobs in acyclic networks (top). Due to the interdependency between tasks 2 and 3, there is no clear execution route in cyclic graphs (bottom). However, it uses the acyclic property of DAGs to resolve and execute these task graphs effectively.
An operator represents a single task and determines what actually executes when the DAG runs. The DAG makes sure that the operators run in the correct order. Compared to the other dependencies, the operators generally run independently on two different machines. Operators are only loaded by Airflow if they are assigned to a DAG. Here are the different operators that you can access here:
Use the BashOperator to execute commands in a Bash shell.
You can use Jinja templates to parameterize the bash_command argument.
Jinja template not found. Add a space after the script name when directly calling a Bash script with the bash_command argument. This is because it tries to apply a Jinja template to it, which will fail.
Use the PythonOperator to execute Python callables.
Passing in arguments
To pass additional arguments to the Python callable, you can use the op_args and op_kwargs arguments.
When you set the provide_context argument to True, it passes in an additional set of keyword arguments: one for each of the Jinja template variables and a templates_dict argument. The templates_dict argument is templated, so each value in the dictionary is evaluated as a Jinja template.
It is referred to as a task once the operator is initiated. The initiation specifies values while calling the operator and the parameterized task becomes a node in a DAG. A task instance represents an indicative state such as “running”, “success”, “failed”, etc.
The hooks are interfaces to external platforms and databases such as S3 (Simple Storage Service), MySQL, HDFS, Hive, etc. They act as building blocks for operators and keep information and the authentication code out of data pipelines centralized in the metadata database.
Airflow in Clouds
Here are a few instances where this Airflow tool can be leveraged seamlessly on Cloud platforms:
It can be deployed in AWS using services such as EFS/S3 (Elastics File System) for storage and Amazon RDS (Relational Database Service) for its metadata database. It also provides various AWS-specific hooks and operators that allow you to integrate with different services with the AWS cloud platform.
2. Google Cloud Platform (GCP)
It provides many GCP-specific hooks and operators that enable users to integrate with different types of services in the Google Cloud Platform. It can be installed with the apache-airflow-providers-google package.
It can be deployed in Azure utilizing services such as Azure File/Blob Storages for storing files and Azure SQL Database for its metadata database. It provides several Azure-specific hooks and operators that allow users to integrate with different services with the Azure cloud platform.
Following are the real-world examples of how this Apache tool helped businesses reach their desired goals.
Adobe is a software company famously known for multimedia and creativity products such as Acrobat Reader and Photoshop. The Adobe Experience Platforms uses Apache Airflow’s plugin interface to write custom operators. Its execution engine is used by the Adobe experience platform orchestration service to schedule and execute various data pipelines. It includes a highly comprehensive airflow web UI that provides data pipeline-related insights.
Plarium is a gaming web platform. It provides over 20 games, including Vikings: War of Clans, The Stormfall franchise, and Raid: Shadow Legends. Creating a cross-platform gaming platform requires a more sophisticated workflow orchestrated for solving tasks related to game development. It comes with a plethora of useful built-in features, including some that are integrative. While creating data pipelines, the DAG model assists users in avoiding mistakes and following the general patterns. As a result, Plarium could manage to simplify the process of building more complex workflows.
Adyen is an e-commerce company, a financial technology platform that provides end-to-end payments, revenue protection, and finance management in a single solution. With the increasing number of users and teams resulting in huge amounts of data, the company faced a lot of issues. To schedule and execute multiple ETL tasks simultaneously was one of the issues. Its already existing operators made it is easier to write ETL DAGs.
Throughout the blog, we learned that Apache Airflow has a simple user interface and provides versatile Python scripting, making it ideal for data management. We also observed the power of DAGs to work efficiently and how they can be deployed and integrated on various cloud platforms. Moreover, we discovered that because of its reliable features, many large companies rely on it for orchestrating critical data pipelines.
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