Apache Airflow is a tool that can create, organize, and monitor workflows. It is open-source hence it is free and has a wide range of support as well. It is one of the most trusted platforms that is used for orchestrating workflows and is widely used and recommended by top data engineers. This tool provides many features like a proper visualization of the data pipelines and workflows, the status of the workflows, the data logs, and codes as well in quite detail. Airflow is a distributed system, that is highly scalable, and can be connected to various sources making it flexible. These features allow it to be used efficiently in the orchestration of complex workflow and data pipelining problems.
Since it is a popular platform there are numerous Apache Airflow Use cases. Also, optimizing the airflow is key to achieving maximum benefits. this article talks about Apache airflow use cases and 5 best practices to improve the airflow.
Table of Contents
What is Apache Airflow?
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Apache Airflow is a tool that monitors and manages data pipelines and complex workflows. It is a popular workflow engine that ensures order is followed by the steps of a data pipeline and they are executed properly. It also ensures all the tasks get the required resources for high efficiency.
Apache Airflow allows to schedule, execute and monitor complex workflows. It is an open-source platform providing it with a lot of support. It provides many features to create the architecture of complex workflows. It is one of the most powerful open source data pipeline platforms in the marketplace.
Airflow incorporates the use of DAG. DAG stands for directed acyclic graphs. All the workflows are represented in form of graphs where the nodes of DAG represent the task. The main motto that airflow follows is that all the workflows can be coded and it became a code-first platform.
Features of Apache Airflow
- Easy useability: Just a little knowledge of python is required to deploy airflow.
- Open Source: It is an Open-source platform making it free to use and that results in a lot of active users.
- numerous Integrations: Platforms like GOogle cloud, Amazon AWS, and many more can be readily integrated using the available integrations.
- Python for coding: beginner-level knowledge of python is sufficient to create complex workflows on airflow.
- User Interface: Airflow’s UI helps in monitoring and managing the workflows.
- Highly Scalable: Airflow can execute thousands of tasks per day simultaneously.
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Apache Airflow Use cases
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Apache is a very popular workflow management tool in the market. It is trusted by many companies. Before the emergence of airflow, the other workflow management tools required multiple configuration files to create a DAG. Airflow just requires a single python file to create DAG and allows for integrations with various sources.
The best Airflow use cases:
- Apache Airflow Use case 1: Airflow is beneficial for batch jobs.
- Apache Airflow Use case 2: Organizing, monitoring, and executing workflows automatically.
- Apache Airflow Use case 3: When the organizing, scheduling of data pipeline workflows is pre-scheduled for a specific time interval airflow can be used efficiently.
- Apache Airflow Use case 4: Airflow can also be used for the ETL pipelines that work on batch data. Also, airflow works well on pipelines that get data from multiple sources or perform data transformation.
- Apache Airflow Use case 5: Airflow can be used for training the machine learning models, and also triggering jobs like a SageMaker.
- Apache Airflow Use case 6: Airflow can be used to generate reports.
- Apache Airflow Use case 7: Apache airflow can be used in scenarios where there is a requirement of backup from DevOps tasks and storing the results into a Hadoop cluster after the execution of a Spark job.
As is the case with many applications, Apache Airflow Use cases are not valid for every scenario. there are many technical considerations that may be required in Apache Airflow use cases to be able to work for your problem statement.
Real-world Apache Airflow use cases include adobe, big fish, Adyen, and many more. To know more about the Airflow use cases, click here.
Airflow Best Practices
Airflow has many functionalities and is very useful in a variety of scenarios. But optimization is the key to achieving the maximum potential of the tool. This section tries to provide the best practices you can follow to get the maximum out of the airflow.
Workflows should be kept updated
- Airflow workflows are based on python code, and for python to run efficiently workflows should be kept up to date.
- This can be achieved by syncing them to the Github repository. Airflow loads files from the DAG folder to the airflow directory, this allows you to create subfolders that can also be linked to the Git repository.
- Bashoperator and pull requests can be used to synchronize the directory. A pull request can be done at the start of the workflow.
- All the files used in workflows like scripts of machine learning models can also be synced using Github.
A proper purpose for DAG:
- Before a DAG is created its purpose should be defined and interpreted properly.
- All the components like the input of DAG and the resultant output, the triggers, the integrations, third-party tools, etc. should be carefully planned.
- The complexity of DAG should be minimum. It must have a clearly defined motto, like exporting data warehouses or updating models. This makes maintenance easier.
Usage of Variables:
- Airflow provides many options to make the DAG flexible. In general execution, a context variable is passed to each workflow. This gets incorporated into a SQL statement. The variable includes information like run ID, run times, execution dates, and many more.
Priorities:
- Priority_weight parameter can be used to control the priority of workflows. This avoids temporary workflows that can occur when multiple workflows compete for execution. Parameters can be set for each task or the entire DAG.
- You can also use multiple schedulers to optimize startup delays of workflows.
Service Level Agreements (SLA):
- The deadline for the entire task to be completed can be defined in Airflow. If the task is not completed by the set deadline, the person in charge is notified and the event is logged. This helps in understanding the cause of the delay and optimizing them for similar situations in the future.
Conclusion
Apache Airflow has been a leading workforce management tool since its introduction. It combines the features such as ease of use, high-level functionality, and many more under a single platform. Airflow can be used under various situations but not all. This article gave a few famous Apache Airflow Use Cases and also a few real-life Apache Airflow Use Case examples. It also gave steps for optimizing the airflow.
Airflow is a trusted source that a lot of companies use as it is an open-source platform. But creating pipelines, installing them on the system, monitoring pipelines, all these are very difficult on Airflow as it is a completely coding platform and it would require a lot of expertise to run properly. This issue can be solved by a platform that creates data pipelines with any code. The Automated data pipeline can be used in place of fully manual solutions to reduce the effort and attain maximum efficiency and this0 is where Hevo comes into the picture. Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations that you can choose from.
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Arsalan is a data science enthusiast with a keen interest towards data analysis and architecture and is interested in writing highly technical content. He has experience writing around 100 articles on various topics related to data industry.
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