Selecting the right tech stack for your organization is important for its success. Learn how you can use Apache Airflow. The use cases of this solution may surprise you – it has multiple business applications. In our article, we explain how Apache Airflow works and when you should consider using it.
Apache Airflow is quite a popular tool for workflow orchestration – especially among developers. It is Python-based and open source, which means that anyone who knows Python can use it for free. Many big companies use it for authoring, scheduling and monitoring workflows. Is it the right solution for your business?
What do you need to know about using Apache Airflow? Features
Apache Airflow is part of a modern data stack for various companies. Why? Organizations use multiple, separate tools to extract, load and transform data, but they can’t communicate without a reliable orchestration platform such as Airflow. This Apache Software Foundation’s tool (first developed by Airbnb) is an open-source project for authoring, scheduling and monitoring data and computing workflows. It uses Python for creating workflows, so it is a good choice for teams that code in Python.
As an open-source solution, it is used widely by businesses all over the world, so its users can count on support from the active community gathered around it. It provides companies with many useful tools for proper visualization of the data pipelines and workflows. As Apache Airflow is a distributed system, it is highly scalable and suitable for big organizations that need smooth integrations with many tools.
When should you consider using Airflow?
Airflow can be used by companies for creating, managing and monitoring data pipelines and complex workflows, which makes it a good tool choice for enterprises. It will allow you to organize your workflows and make sure that all tasks will be provided with the required amount of resources, which ensures the high efficiency of your processes.
You should consider it, especially, if your organization works with data that comes from multiple sources. Apache Airflow is well-adjusted for companies that rely on batch information processing or need reliable, automated reporting. It is also often leveraged by businesses leveraging machine learning models and by DevOps teams.
Apache Airflow use cases
Because of Apache Airflow’s versatility, you can use it to set up any type of workflow. In general, it is good for pipelines related to a certain time interval, or those that are pre-scheduled, but it can also run ad hoc random workflows not related to any schedule. Check out some real time Apache Airflow use cases.
Batch data processing
Apache Airflow is known as a platform for developing and monitoring batch data pipelines. It does a good job of orchestrating batch jobs and provides automation to many processes, such as organizing, executing, and monitoring data flow. It is most suitable for data pipelines that change slowly after deployment (in days or weeks instead of minutes or hours). Airflow can be used by companies that extract batch data from multiple sources and perform data transformation regularly.
Airflow makes working on data easier, because it serves as a framework for integrating data pipelines of different technologies. Workflows created on this platform are coded in Python, and the user can easily enable communication between multiple solutions, even though Airflow itself is not a data processing tool.
Each business deals with data and reporting. Many companies send weekly or monthly reports to their partners, to provide them with crucial information about the products. It takes some time and energy to create an easy to understand, attractive report based on a massive amount of data. Creating a detailed report with visualizations manually can be really time-consuming. Fortunately, Airflow comes with automated reporting features.
With Apache Airflow, you can schedule your automatic reports according to your individual needs. All you need to do is to define a DAG for each of your requirements. Airflow has a built-in Reporting Automation model that lets any member of your IT team create unique schedule reports. More importantly, Airflow is intuitive when it comes to reporting, so you can do it in no time.
Machine learning projects are rather complex, but their success depends heavily on the quality of data used for training the ML models. So, one of the most significant tasks you have to carry out is data validation. During this process, you check if your data is accurate, complete, and meaningful. But how do you efficiently validate a large number of big datasets? The answer is: through automated validation checks – and that is where Airflow comes in.
The process of validating data should be performed as efficiently, but also precisely, as possible. You should not train your ML model on invalid data, so you can’t really move forward before you perform all necessary checks. Apache Airflows does not require a lot of implementation effort, which is why you can’t go straight to the validation process. This tool provides users with a group of check operators, which enable easy and fast data quality verification. This solution makes it simpler to spot what the problem is that you have to deal with.
Airflow enables you to build elements of a machine learning pipeline effectively. Reusable code components can be used in different machine learning models and datasets, so the code is clean and concise. You can monitor your data and models and all processes carried out on them on the platform and control the scalability. A wide range of available operators, hooks, and modules makes Airflow a multi-purpose tool great for many organizations.
Various DevOps tasks
ETL tools are used for extraction, transformation and loading data – so basically, they are indispensable for many companies which leverage Business Intelligence and Analytics and tend to make data-driven decisions. The best ETL solution should handle complex schemas and massive amounts of data (Big Data).
Airflow is not specifically an ETL tool, but you can use it to manage ETL processes in BI and analytics environments. With this platform, you can manage, structure and organize ETL pipelines using DAGs. Directed Acyclic Graphs (DAGs) can form relationships and dependencies with no need to define tasks, so this is a simple and efficient way to deal with DevOps processes. Airflow will make a fine addition to a DevOps tech stack. It has Google Cloud and AWS hooks and operators, which simplifies working in cloud warehousing environments.
To sum up
Airflow can be a useful addition to your modern data stack. It provides you with tools for easy defining, scheduling, visualizing and monitoring workflows. It is scalable and has multiple applications. What is more important, it is used by many companies all over the world, so they have already tested it in real life projects. This tool requires coding skills and fluency in Python. Are you still not sure if Apache Airflow is the right tool for your business? Contact us and tell us more about your current tech stack and the challenges you deal with on a daily basis. We will analyze your requirements and help you improve your processes.
Learn more about Airflow:
- A more efficient scheduler to improve performance in Airflow 2.0
- Powerful REST API in Airflow 2.0 — what do you need to know?
- How to simplify Airflow 2.0 setup with the DAG Versioning and Serialization