![]() However, unlike Airflow, UAC is designed with enterprise-grade capabilities that enable building and operating data pipelines as mission-critical systems.īecause UAC is vendor agnostic, it’s designed to work across all the different tools used along the data pipeline. More specifically, UAC is used for DataOps orchestration. Much like Airflow, UAC includes a scheduler and workflow engine. ![]() With roots in the workload automation world, UAC is what Gartner refers to as a DataOps tool. How Do I Connect to and Trigger Events with the Airflow API? Generally, larger organizations with complex data pipelines will opt for the Airflow API. But that brings us to an important question that we’ll cover below. While it’s a minuscule amount per call, it really starts to add up over time.Īll of these above factors equal extra lift and multiple points of failure - which you can’t really see if it breaks down. Most cloud providers charge for API calls. Constantly checking for system events requires polling via the API.If you use the Airflow API, you have a handful of API configurations to complete and security to worry about.If a sensor or deferrable operator does not yet exist, you’ll have to write one from scratch.As you can imagine, managing a bunch of schedulers in addition to the Airflow scheduler can really get complex. Each of these tools in your pipeline would need to use that tool’s associated job scheduler. In a one-off scenario, this approach will work.īut what happens when you’re not exclusively using AWS for your data pipeline? Often, you wind up needing a different job scheduler for each data tool used along your pipeline.įor example, let’s say your pipeline runs across AWS, Azure, Informatica, Snowflake, Databricks, and PowerBI. ![]() Each of the above-described methods typically requires a third-party scheduler to send the trigger.įor example, if you’re a developer who wants to trigger a DAG when a file is dropped into an AWS S3 bucket, you may opt to use AWS Lambda to schedule the trigger. Triggering a DAG based on a system event from a third-party tool remains complex. Limitations to Event-Based Automation in Airflow Trigger a DAG when a Kafka or ASW SQS event is received.Trigger a DAG when a data file is dropped into a cloud bucket.Trigger a DAG when someone fills in a website form. ![]() In the original Airflow, it was considered experimental.Ī few examples of what you might automate using sensors, deferrable operators, or Airflow’s API include:
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