Apache Airflow workflow management lets you author and schedule data pipelines and automate workflow activities easily. You build workflows with directed acyclic graphs (DAGs).
A DAG consists of nodes and connectors (edges). You can start at any node and travel through all connectors, and you traverse each connector only once. The topologies of networks and trees are different types of DAGs.
Workflows based on Airflow have tasks whose outputs are inputs for other tasks. Consequently, the ETL process also qualifies as part of the DAG. You cannot loop back, because the output of every step is the input of the next step.
Hence, Apache Airflow changes the way teams manage data. Code-defined workflows make maintenance, testing, and version management easier.
How Is Apache Airflow Workflow Management Helping Businesses?
You can manage your regular work using Apache Airflow, an open-source scheduling tool. It is an excellent tool to monitor, organize, and run your workflows seamlessly.
Apache Airflow solved a number of problems that similar tools commonly faced in the past. Here is how Apache Airflow makes workflow management seamless for businesses that process data and manage regular work.
DAGs in Apache Airflow Workflow Management
With DAGs, you can create workflows in which you retry individual operations if they fail. You can also restart an operation after a failure. In addition, DAGs let you abstract an assortment of operations.
You can automate Python code, queries, and Jupyter notebooks using Airflow.
Airflow provides a variety of operators for executing code. The Python Operator makes Python code portable quickly, because Airflow is written in Python and works with most databases.
Further, the PapermillOperator is a plugin for Jupyter notebooks. It lets you parametrize and run notebooks. For example, Netflix has suggested combining Airflow with Papermill to automate and deploy notebooks in production.
Management Of Task Dependencies
With a specific sensor, Airflow manages all kinds of dependencies efficiently, including a DAG run status, task completion, partition presence, and file presence. In addition to task dependency concepts, Airflow also supports branching.
Extendable Model
You can extend Airflow by adding custom operators, sensors, and hooks. The community-contributed operators are a very helpful part of Airflow’s success.
Developers use Python wrappers to create operators for other programming languages, such as R[AIRFLOW-2193]. Javascript may also get a Python wrapper (pyv8) in the near future.
Management And Monitoring Interface
With the Airflow management and monitoring interface, you can take an overview of tasks. You can also clear and trigger these tasks and DAG runs.
Scheduling
The scheduler runs your tasks at the frequency you specify. First, it finds all eligible DAGs, and then it puts them in a queue. If retry is enabled for a DAG, the scheduler automatically retries a failed DAG. However, there are specific limits on retries at every DAG level.
Webserver
Airflow uses the webserver as its frontend. From the UI, a user can enable and disable a DAG, retry it, and view its logs.
The UI also shows which tasks failed, why they failed, how long they took to run, and when they were last retried.
Therefore, Airflow’s user interface gives it an edge over its competitors. In Apache Oozie, for example, viewing logs for non-MR (map-reduce) jobs can be difficult, but Apache Airflow does not have such complications.
Backend
Airflow stores all DAG and task run data, and its configuration, in MySQL or PostgreSQL. The SQLite backend comes installed by default, so you need no additional setup.
Conclusion
A Python script defines the Airflow DAG object. Another Python script can then use this object to implement the ETL process.
The Apache Airflow data toolbox lets users develop their own plugins. By adding plugins, you can add features, interrogate platforms effectively, and handle more complex metadata and data interactions.
Airflow, in addition to all the benefits listed above, also integrates seamlessly with the platforms in the big data ecosystem, like Spark and Hadoop. It also requires very little planning and time, because you write all code in Python. If you need help, see our custom software services.
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