IN a busy e-commerce company, project managers were overwhelmed by manual tasks and constant delays. The team struggled with managing inventory, processing orders, and generating reports, all of which required intensive manual effort and supervision. Seeking a solution, they turned to Apache Airflow, a powerful workflow automation tool.
With Airflow, the team could define and automate complex workflows effortlessly. Tasks that once consumed hours, such as data extraction and report generation, were streamlined into automated processes. This transformation allowed the team to shift focus from routine management to strategic planning, resulting in remarkable efficiency gains and time savings.
In this blog post, we’ll explore how Apache Airflow can revolutionize your workflow automation, using real-life examples to highlight its powerful capabilities and practical benefits.
What is Apache Airflow?
Apache Airflow is an open-source tool to programmatically author, schedule, and monitor workflows. It is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. You can easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status.
With Airflow, users can author workflows as Directed Acyclic Graphs (DAGs) of tasks. Airflow’s rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. It connects with multiple data sources and can send an alert via email or Slack when a task completes or fails. Airflow is distributed, scalable, and flexible, making it well-suited to handle the orchestration of complex business logic.
Airflow Architecture:
The Airflow platform lets you build and run workflows, which are represented as Directed Acyclic Graphs (DAGs). A sample DAG is shown in the diagram below.
A DAG contains Tasks (action items) and specifies the dependencies between them and the order in which they are executed. A Scheduler handles scheduled workflows and submits Tasks to the Executor, which runs them. The Executor pushes tasks to workers.
Other typical components of an Airflow architecture include a database to store state metadata, a web server used to inspect and debug Tasks and DAGs, and a folder containing the DAG files.
Key features of Airflow:
Let’s take an example from real life scenario:
Imagine Airflow as a factory assembly line where various components work together to complete a product.
Mapping Airflow Components to Assembly Line Parts:
- Scheduler: The factory manager who decides the order in which tasks should be performed.
- Worker: The assembly line workers who carry out specific tasks.
- Web Server: The control room where supervisors monitor the progress.
- Metadata Database: The factory’s record-keeping system that tracks the progress and state of each product.
Basic Airflow deployment
This is the simplest deployment of Airflow, usually operated and managed on a single machine. Such a deployment usually uses the LocalExecutor, where the scheduler and the workers are in the same Python process and the DAG files are read directly from the local filesystem by the scheduler. The web server runs on the same machine as the scheduler. There is no trigger component, which means that task deferral is not possible.
Such an installation typically does not separate user roles – deployment, configuration, operation, authoring and maintenance are all done by the same person and there are no security perimeters between the components.
If you want to run Airflow on a single machine in a simple single-machine setup, you can skip the more complex diagrams below and go straight to the Workloads section.
Uses of Airflow
Imagine you have an ML model that does twitter sentiment analysis. Now you want to run that model for your favorite people on twitter for their tweets every day. Such a workflow would look something like this.
As you can see, the data flows from one end of the pipeline to the other end. There can be branches, but no cycles.
What problems does Airflow solve?
- Crons are an age-old way of scheduling tasks.
- With cron, creating and maintaining a relationship between tasks is a nightmare, whereas, in Airflow, it is as simple as writing Python code.
- Cron needs external support to log, track, and manage tasks. Airflow UI to track and monitor the workflow execution
- Cron jobs are not reproducible unless externally configured. The Airflow keeps an audit trail of all tasks executed.
Apache Airflow: 3 Real-Life Stories
There are major enterprises taking advantage of the REST API for a number of use cases, including ETL, MLOps, workflow scheduling, and data processing.
Services Provided by eDgeWrapper
eDgeWrapper offers a comprehensive suite of Airflow-related services designed to meet diverse business needs:
Apache Airflow services we perform:
- Deploying and monitoring Airflow instances.
- Migrating Airflow instances.
- Migrating workflows
Upgrading Airflow to newest versions. - Resolving issues with Airflow components
Spotting and fixing Airflow bugs. - Writing DAGs with all kinds of operators
Writing custom plugins.
Working Process at eDgeWrapper
Our working process is meticulously crafted to deliver high-quality Airflow solutions with precision and efficiency. Here’s a detailed look at our approach:
- Requirement Analysis: Gathering detailed requirements and performing a comprehensive analysis to identify the best workflow solutions.
- Design and Development: Creating detailed workflow designs and developing custom Airflow DAGs (Directed Acyclic Graphs) to meet client specifications.
- Testing and Validation: Rigorous testing of workflows to ensure they function correctly and efficiently under various conditions.
- Deployment: Deploying workflows into the client’s environment, ensuring seamless integration with existing systems.
- Monitoring and Optimization: Continuously monitoring workflows and making necessary optimizations to maintain performance and reliability.
By following this structured working process, eDgeWrapper ensures the delivery of top-notch Airflow solutions that drive efficiency, reliability, and success for our clients.
Let’s talk about a real life eDgeWrapper project scenario:
Suppose we have an application for order placement, where we need to send email to customers for order placement confirmation link, link expiration, order cancel confirmation, order confirmation reminder.
We can define these four tasks in Airflow when the status of the product changes to sent, resent, canceled, expired. We can define a “schedule_interval” of 1 hour, for every 1 hour the airflow dag (created with python operator) will run and check the status of the product in the database, based on the status it can run the task that needs to be executed.
So, this way we can automate the email flow and we don’t have to send email to the user one by one manually.
Conclusion
Mastering Airflow with eDgeWrapper means unlocking the full potential of workflow automation for your business. Our expertise in Airflow, combined with a client-centric approach and a robust working process, ensures that you receive top-notch solutions tailored to your needs. Whether you’re looking to optimize data pipelines, enhance machine learning workflows, or streamline DevOps processes, Contact Us, who is your trusted partner in achieving operational excellence with Apache Airflow.
