What are the best practices for using Airflow to schedule data workflows?

There are many things that a Data Engineer should consider while scheduling pipeline using Airflow 1. Modular Dag: Each DAG should represent a specific workflow or task. This makes it easier to manage and maintain your 2. Parameterize Dag: Parameterize your DAGs to make them dynamic. Instead of hard codingSet Up values, use Airflow's templating features to pass parameters like dates, file paths, or database connections dynamically 3. Set Up Logging and Alerts: Setup email, slack, xMatters alert in case of failure. 4. Keep DAG light weight: One needs to understand that Airflow DAG is for scheduling and monitoring, it should not be used for heavy processing. 5.Scaling: Plan for scaling your Airflow deployment as your workload grows.

Modularize your workflows. Break down your workflows into smaller, reusable components (DAGs) for better maintainability. Avoid monolithic DAGs. Use idempotent tasks. Ensure tasks are idempotent to prevent issues when tasks are retried. Parameterize your DAGs and tasks. Use Airflow templating to parameterize DAGs and tasks. Store parameters in Airflow variables or external systems. Use hooks and sensors. Use timetables. Timetables allow you to define complex scheduling patterns for your DAGs. Monitor your workflows. Monitor your workflows closely to ensure that they are running as expected. Airflow provides a number of tools for monitoring your workflows, including the Airflow UI and the Airflow CLI.

Some best practices for using Airflow to schedule data workflows are: - Use a consistent naming convention for DAGs, tasks, and variables - Organize your DAGs into logical groups or categories - Use Airflow hooks and operators to interact with external systems and services - Define dependencies and triggers between tasks using the >> and << operators - Use the airflow.cfg file to configure global settings and parameters - Test and debug your DAGs locally before deploying them to production - Monitor and troubleshoot your DAG runs using the Airflow web UI or CLI - Document your DAGs using docstrings and comments

using descriptive names is a simple yet powerful practice that significantly improves the quality and maintainability of your code and data workflows. Clear and meaningful names are an investment in the long-term viability of your projects and contribute to effective collaboration within your tea

Few more - Modularization: Break workflows into smaller, reusable components (DAGs) for better maintainability. Avoid monolithic DAGs. Idempotent Tasks: Ensure tasks are idempotent to prevent issues when tasks are retried. Parameterization: Use Airflow templating to parameterize DAGs and tasks. Store parameters in Airflow variables or external systems. Dynamic DAG Generation: Employ Python scripts to generate DAGs dynamically based on configuration files or data dependencies. Use of Sensors: Use Airflow Sensors for waiting on external conditions rather than time-based delays. Task Dependency Configuration: Clearly define dependencies between tasks using set_upstream and set_downstream methods.Document dependencies for DAG clarity.

Use Async Many airflow operators and sensors provides an async mode so that they don't occupy CPU slots when the tasks are being executed. It is recommended to use async operators and sensors to efficiently utilise resources.

Jinja templating can become very powerful, also to simplify SQL workloads, for example: {% for name in ["a", "b", "c"] %} INSERT INTO TABLE some_table SELECT {{ range(1, 100) | random }} AS some_number, "{{ name }}" AS name; {% endfor %} This would reduce duplication when similar queries have to be executed. Still, be careful when using macros. If you used Airflow for some time I guarantee that macros like {{ ds }} resulted into dates that you were not expecting ;).

When running pipelines, it may be wise to group different DAGs/Tasks into groups and models according to the business needs. For eg, if you have a DAG/Tasks for a table, you might want to group all tables related to users into a user model, which triggers all DAGs/Tasks related to users in a pre-defined sequence. You can achieve the above using subDAG, which is now replaced with task groups. On task-level grouping, utilize the task group library in airflow to group your tasks according to business logic. On DAG level, you can also group DAGs by utilizing the trigger dag operator and dummy operators to trigger specific DAGs in parallel and in sequence. Do this by creating a master DAG that triggers other DAGs according to your design.

Using task groups is a great way to structure your DAG. It can also be used as implicit documentation, you can even add a tooltip parameter: with TaskGroup("some_name", tooltip="These tasks are doing XYZ") as some_group: This is then nicely rendered in the Airflow UI.

adhering to best coding practices is not merely a matter of preference; it is a cornerstone of responsible and effective software development. These practices are the result of years of collective wisdom from the software engineering community and serve as a blueprint for writing code that is readable, maintainable, and reliable

There are SO MANY performance bottlenecks in Airflow. At a high level, if you run Airflow on a single VM you'll run into the following Concurrency Limitations + scheduling latency: Understand and fine-tune configs to allow for max task throughput your resources can reliably handle. Out of memory, webserver timeouts/unreliability: Ensure you're not doing expensive things in top level DAG code. Otherwise, add more CPU/RAM. Consider moving airflow internal db to its own instance if you haven't already. Diminishing returns on adding more resources : Eventually Upgrading to a larger VM won't cut it. You'll have to distribute Airflow to multiple VMs and use something other than the sequential executor. I personally favor Kubernetes executor