When you are working on your operational processes and trying to achieve higher operational efficiency through workload automation, sometimes, the solutions can backfire. Automation is a highly technical and advanced technology, and you have to go by it with caution.
When you are automating your processes, you have to create data pipelines and sometimes managing these pipelines can become very hectic, time-consuming, and challenging. Sometimes the pipelines may fail to execute the scheduled tasks, show error in the tasks, or may not perform some tasks as per the expectations. This can make you fall into the hassles of constantly monitoring the pipelines trying to fix the issues, and keeping a check on the tasks, which entirely fails the purpose of automation. You may as well begin to think about why you used automation in the first place, and it would’ve been better if you performed the task manually.
But you don’t need to get worked up like that, especially when you can use technologically advanced and smart solutions like Apache Airflow Tool.
How is Apache Airflow Helpful?
Apache Airflow is a highly advanced and smart workflow management solution that helps you manage your routine and regular business operations, jobs, and tasks. It’s an open-source scheduler using which you can create robust and functional data pipelines to schedule, organize, execute, monitor and manage all your workflows.
Now, that’s what other schedulers do too, so what makes Apache Airflow more helpful and a better solution?
It’s simply the more advanced features and functionalities of the tool. With Apache Airflow, you can easily orchestrate multiple data pipelines simultaneously and make sure that they don’t fail or perform below expectations.
The tool also allows you to set frequency, triggers, rescheduling, retrying, etc., for the scheduled tasks and jobs, thus making workflow management a breeze and coming as a great solution for eliminating all the struggles to maintain and manage data pipelines.
Moving further, this article will throw light on every benefit that makes Apache Airflow Workflow Management Tool an ideal solution to keep up with the most efficient job scheduling and operations. However, before you can truly understand those benefits, it’s important that you have knowledge of the architecture of this tool. So, let’s talk about that first!
Apache Airflow Architecture – How It Works?
Apache Airflow Tool works by using DAG pipelines. These are Directed Acrylic Graphs that represent the workflows that you need to execute. These pipelines are further divided into nodes, and each node represents a particular task that has to be run within the DAG. The user can specify and mention the frequency at which the tasks are to be performed, and if the tasks are dependent on certain triggers, the user can specify the same as well in the DAGs.
These DAGs are created and executed efficiently with synchronization between all the core components of the tool.
Core Components of Apache Airflow Tool
Apache Airflow is made with four core component: a front-end, a backend, a scheduler, and an executor. As these components work in tandem with each other, you are able to leverage amazing automation capabilities with better scheduled, automated, and managed jobs and tasks.
This is where you schedule all the tasks. When you create a DAG, the scheduler looks for it and queues it for execution. It also keeps a check on how the DAGs are performing, takes notice of a failed DAG, and automatically reschedules it if you have enabled retry in the DAG. So, all your tasks are queued, and you can be sure that the data pipelines are automated to perform at their best capabilities.
A webserver is the front-end of Apache Airflow Tool, which serves as a UI where you can monitor the DAGs and keep a note of their status. You can also access this front-end to see the task logs, enable or disable or retry scheduled DAGs and monitor the same to ensure efficient performance.
This is responsible for performing the tasks and workflows queued by the Scheduler. It determines the number of tasks that the tool can execute and perform simultaneously, and that also depends on the executor’s choice of worker for the DAG. The executor also constantly reads the tasks and updates their status in the logs.
However, you need to keep caution when using Executor. A by default setting in executor makes it use SerialExecutor, which allows running only one task at a time, and that’s not advisable at all. One by one execution of the tasks can delay the processes. So, you must look through the default settings and change the same as per your business needs and requirements.
The backend supports smooth running of the tool. The code is Python-based, and that’s what makes it easier for you to start using the Apache Airflow tool. For the storage of configuration and other data, the tool uses MySQL or PostgreSQL. For the setup and running, the tool has SQLite for the backend, due to which you need not go through any additional setup requirements. However, using this backend can be risky as there’s a high probability of data loss with it. So, while it will take you some time to set up the tool with some other backend, it’s in your favour, and everything will be worth the time you give to setup.
So, this is a small brief introduction of the Apache Airflow Tool and how it works. Now that you have an understanding and know-how of the tool, you can make out that it’s a great workflow management solution and can overcome all the challenges and struggles you may have been facing to manage automated data pipelines. Apache Airflow makes automation a breeze, and that’s what makes it a great solution.
Here are some more points on why it’s perfect for eliminating the struggles and challenges of maintaining the data pipelines.
How is Apache Airflow Tool the Ideal Solution?
Apache Airflow is the most robust workflow management solution you can find today. Apache has efficiently worked on it and introduced features and functionalities that outnumber any other solution or even its predecessors themselves. Here are some benefits that make Apache an ideal solution for managing data pipelines and workflows.
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Constant Monitoring of Data Pipelines
If you want to manage your data pipelines well, you have to make sure that they are performing consistently, and that requires constant monitoring of the DAG execution and performance. Apache Airflow constantly monitors the task status and displays it on the front-end UI along with detailed logs on task execution and performance. Additionally, if any DAG fails or is rescheduled, the tool sends you instant alerts through emails. It also gives metrics on what tasks were monitored, what tasks were retried, how many times they were retried, the last try made, why the task failed, etc. With all such metrics, you can easily find the issues with your data pipelines and make sure that they are managed well.
Lineage Tracking of Data Sources
lineage helps you depict and analyze the relationship between those sources along with making it easier for you to manage them. This is a relatively new feature that’s introduced in the latest version update of Apache Airflow. With Lineage, you can easily track down all the data sources, where they have come from, where they are going, and what’s happening to those sources. So, you can easily handle multiple data tasks with the ability to show lineage between the input and output data sources by creating graphs. That lineage helps you depict and analyze the relationship between those sources along with making it easier for you to manage them.
Manage Data Pipelines with Triggers
Apache Airflow has Sensors that you can use to manage the tasks and data pipelines based on triggers. You can set a precondition for the tasks to be performed along with the frequency at which the tool must check for the set triggers. Moreover, you can specify the types of sensors based on your requirements which further simplifies the management of pipelines.
Ability to Customize the Pipelines
Customization can be the best tool in your hands when you want a solution specific to and personalized for your needs and requirements. Along with its already feature-rich deliverables, Apache Airflow Tool provides for scalable customization abilities using which you can create your own data pipelines, operators, and sensors and mould them as per your requirements. That gives you an even more personalized experience, along with the ability to consistently manage the data pipelines.
Are You Ready to Leverage the Solution?
If you are looking for experts, try our Apache Airflow Solutions and Services, and we can leverage the benefits of managing and maintaining great data pipelines at scale and create synergies.