If you’re a data engineer, you know this feeling. It’s 3 a.m., and you’re awake. Not because you want to be, but because you receive a frantic call from an executive in another part of the world who’s waiting for a critical business report that was supposed to be available an hour ago. You jump to your Airflow UI, locate the associated DAG, and find a sea of green. Every task is marked "success."
However, the report is empty.
You start the frantic process of elimination. You begin to analyze worker logs and manually inspect task outputs, all while the clock is ticking. Three hours later, you find the problem: an upstream file transfer process, running on a completely different system, encountered an issue and delivered a zero-byte file. Airflow did its job perfectly—it ran every task as instructed. But it was blind to the world outside its own logic, and that blindness just cost you three hours of sleep and caused a major business disruption.
This isn’t a failure of Airflow. It’s a symptom of its success. As Airflow becomes central to enterprise data operations, our pipelines inevitably become part of larger, more complex workflows that cross system boundaries. And in those scenarios, the Airflow UI, as useful as it is, is only telling you half the story.
The cross-DAG dependency maze
Experienced Airflow users are well aware of this challenge. To manage dependencies between DAGs and other systems, we’ve developed a toolkit of common patterns. But each comes with its own set of painful trade-offs.
The brittle ExternalTaskSensor
The classic tool for this job is the ExternalTaskSensor. Its job is to pause a DAG execution, or make it wait until a task in another DAG completes. While functional, it’s notoriously brittle in production. Here are just a few of the areas in which issues arise:
- Schedule synchronization. The sensor relies on matching execution_date. This becomes a major headache when DAGs have different schedules, or one is triggered manually. Many a developer has been stumped by a sensor that pokes forever because of a subtle mismatch in start times or a misunderstood execution_delta.
- Worker slot occupancy. In its default poke mode, a sensor occupies a full worker slot while it waits. If you have dozens of sensors waiting on long-running upstream jobs, you can easily exhaust your worker capacity, creating system-wide bottlenecks. The reschedule mode helps, but adds scheduler overhead.
- Limited failure states. Before Airflow 2.0, a sensor would poke indefinitely if the upstream task failed, as it only watched for SUCCESS by default. While newer versions have failed_states, configuring complex dependency logic remains a challenge.
The promise and limits of datasets
Introduced in Airflow 2.4, Datasets provide a more elegant, event-driven approach. A "producer" task can declare a Dataset as an outlet, and a "consumer" DAG can run whenever that Dataset is updated. This is a fantastic step forward for data-aware scheduling.
However, Datasets have a fundamental limitation: they are an Airflow-only concept.
The "Dataset" is just a URI string that Airflow uses for internal signaling. It has no knowledge of the data's actual content, location, or state. It cannot tell you if your S3 file has arrived in a corrupt state, or if the table it represents in Snowflake hasn't been populated by the upstream legacy ETL tool. Synchronizing Datasets across separate Airflow instances requires custom, high-frequency DAGs or external services, adding significant complexity.
These tools are essential, but they only solve a piece of the puzzle within the Airflow universe. The 3 a.m. problem I described earlier happens because our data pipelines don't live solely in the Airflow universe.
Beyond the DAG: The rise of enterprise-wide observability
The real problem isn't just about cross-DAG dependencies; it's about cross-domain orchestration. A modern enterprise data workflow might look like this:
- A mainframe job (scheduled by AutoSys) extracts raw financial data.
- A managed file transfer (MFT) process moves the file to cloud storage.
- An Airflow DAG is triggered to ingest, clean, and transform the data in Snowflake.
- A second Airflow instance (managed by a different team) runs machine learning models on that transformed data.
- A business intelligence tool like Tableau is refreshed.
No single tool in this chain has the full picture. Your data platform team lives in a world of siloed views, reacting to problems instead of preventing them. This is why we need to shift our thinking from orchestration to enterprise-wide observability.
Data observability isn't just about monitoring; it's about gaining a deep understanding of your system's health by unifying metrics, logs, and lineage from every component in your stack. An enterprise observability platform acts as a "single pane of glass" that sits above individual tools like Airflow, MFT, and other schedulers. It gives you the end-to-end context that you’re missing.
Uncloaking your pipelines: A predictive intelligence platform in action
This architectural pattern is precisely what Broadcom's Automation Analytics & Intelligence (AAI) platform delivers. It integrates with Airflow and many other enterprise automation tools to ingest their metadata and stitch together the complete story.
Let's see how this solves our problems.
For the data engineer: Remember the 3 a.m. alert? Instead of diving straight into worker logs, you look at the AAI pipeline view.
Here, you can instantly see the entire "Expense Payment Processing" workflow. You see the Expense-Generation job (running in a separate scheduler) and its connection to the Expense-Processing Airflow DAG. AAI will immediately show you that there was an issue with upstream processing. This visibility turns a three-hour debugging nightmare into a three-minute diagnosis. It provides the end-to-end data lineage that is crucial for rapid root cause analysis.
For the data platform lead: Your concerns are broader: Are we meeting SLAs? What is our resource usage? How do we prove our ROI? Instead of trying to aggregate metrics from multiple Airflow instances, you get a unified dashboard.
This view gives you real-time service delivery tracking, performance analytics, and auditability across all platforms. You can finally answer the question, "Is our entire data automation landscape healthy?" and have data-driven evidence at your fingertips to prove it.
Elevate Airflow from a tactical tool to a strategic asset
Airflow is, and will remain, a best-in-class tool for authoring and executing data pipelines. But to make it truly effective in the enterprise, we need to stop looking at it in isolation.
By integrating Airflow into a broader enterprise observability platform, you empower it. You transform your data operations from:
- Siloed → unified visibility
- Reactive → proactive monitoring
- Guesswork → data-driven decisions
Remove the blindfold—stop navigating your critical data pipelines in a void of darkness. Instead, look to the horizon and finally see the whole story.
