A Data Observability Model for Data Engineers
We believe the world needs better data observability. To believe that, you must also believe the opposite is true, which we do—that very little that happens in data engineering today is observable. Most pipelines are built to move but not monitor. To measure, but not track. To transform, but not tell. The result is the infamous case of the black box.
Beware the black box scenario
You know what goes in. You know what comes out. But what happens in between? And why the discrepancy? Sadly these are mysteries most pipelines were not built to solve. Most were designed for the best-case scenario. Yet reality is of course more closely governed by Murphy’s law, and on the output side of the black box, along with a host of strange values and cryptic missing columns, are legions of data engineers scratching their heads and realizing that to correct, you must first observe.
This guide will cover the following points:
What is data observability?
“Observability” has become a bit of a buzzword so it’s probably best to define it: Data observability is the blanket term for monitoring and improving the health of data within applications and systems like data pipelines.
Data observability vs. monitoring?
“Data monitoring” lets you know the current state of your data pipeline or your data. It tells you whether the data is complete, accurate, and fresh. It tells you whether your pipelines have succeeded or failed. Data monitoring can show you if things are working or broken, but it doesn’t give you much context outside of that.
As such, monitoring is only one function of observability. “Data observability” is an umbrella term that includes:
- Monitoring—a dashboard that provides an operational view of your pipeline or system
- Alerting—both for expected events and anomalies
- Tracking—ability to set and track specific events
- Comparisons—monitoring over time, with alerts for anomalies
- Analysis—automated issue detection that adapts to your pipeline and data health
- Next best action—recommended actions to fix errors
By encompassing not just one activity—monitoring—but rather a basket of activities, observability is much more useful to engineers. Data observability doesn’t stop at describing the problem. It provides context and suggestions to help solve it.
“Data observability goes deeper than monitoring by adding more context to system metrics, providing a deeper view of system operations, and indicating whether engineers need to step in and apply a fix,” explains Evgeny Shulman, Co-Founder and CTO of Databand.ai. “In other words, while monitoring tells you that some microservice is consuming a given amount of resources, observability tells you that its current state is associated with critical failures, and you need to intervene.”
“Observability tells you that its current state is associated with critical failures, and you need to intervene.”
– Evgeny Shulman, Co-Founder and CTO, Databand.ai
This proactive approach is particularly important when it comes to data pipelines.
The role of observability tools
Data pipeline observability is the ability to know not simply that your pipeline failed, as monitoring would tell you. A data observability tool does the detective work to point you to the proximal cause—a Spark job failed—as well as the root cause—the data contained an invalid row. These tools are also able to pick up on observability patterns using machine learning and anomaly detection, so you know when values broke out of an expected range.
Why is data observability so important for pipelines?
Data pipeline observability matters because pipelines have gone from complicated to complex—from many concurrent systems to many interdependent systems.
Pipelines are essential to a rapidly expanding industry
It’s more likely than ever that software applications don’t just benefit from data pipelines—they rely on them. As do end users. When big providers like AWS have outages and the dashboards of applications around the world blink out of existence, you can see the signs all around you that complexity creates dangerous dependencies.
Right now, the analytics industry has a combined annual growth rate of 12% per year. It will be worth an astounding $105 billion by 2027, according to Garnter—about the size of Ukraine’s economy. At this rate, corporate data volume is currently increasing 62% every month. All those businesses storing and analyzing all that data? They’re betting their business on it and that the data pipelines that run it will continue to work.
Context is crucial (and often lacking)
A major cause of data quality issues and pipeline failures are transformations within those pipelines. Most data architecture today is opaque—you can’t tell what’s happening inside. Transformations are happening, but when things come out not as expected, data engineers don’t have a lot of context for why.
Too many DataOps teams spend far too much time trying to diagnose issues without context. And if you follow your first instinct and use a software application performance management tool (APM) to monitor a DataOps pipeline, it rarely works out.
Observability keeps engineers in sync (and confident)
“Data pipelines behave very differently than software applications and infrastructure,” says Evgeny. “Data Engineering teams can gain insight into high-level job (or DAG) statuses and summary database performance but will lack visibility into the right level of information they need to manage their pipelines. This gap causes many teams to spend a lot of time tracking issues or work in a state of constant paranoia.”
Having a bigger and more specialized data team can help, but it can hurt if those team members don’t coordinate. More people accessing the data and running their own pipelines and their own transformations causes errors and impacts data stability.
More and more engineers today are concerned about data stability, and whether their data is fit for use by its consumers, within and without the business. And so, more teams are interested in data observability.
How data observability platforms can help
Data observability platforms provide insight monitoring tools alone cannot. They tell you not simply what went wrong, but what problems it’s causing and offer clues and even next-best-actions for how to fix it. It does this continuously, without you having to re-architect your current pipelines or “change the engine while in flight,” as it were.
Why engineers adopt observability platforms
- Your data pipelines are complex systems. They require data observability architecture that conducts constant sleuthing.
- You need to know where things failed, and why. An observability platform provides end-to-end monitoring for that very purpose.
- You need a way to track downstream dependencies, and know, not hope, that your fix addressed the root problem.
Components of an effective observability platform
Your data pipelines are complex systems and they require data observability architecture that conducts constant sleuthing. You need an observability platform for end-to-end monitoring so you know where things failed, and why. You need a way to track downstream dependencies, and know, not hope, that your fix addressed the root problem.
A data observability platform should include:
- Simple setup—does it require changing your pipeline?
- End-to-end tracking—can it monitor downstream dependencies?
- Observability architecture—does more than just monitoring
- Threshold setting—can it do its own anomaly detection?
- Administration—can it monitor data at rest?
- Data observability open source—does it provide open source components you can adjust?
- Distributed systems observability—can you observe distributed systems as well?
The platform should also offer plenty of prescriptive guidance. The field of data observability and data engineering is moving quickly, and one of the best ways to find a platform that’s evolving as fast as your problems. It isn’t enough to monitor anymore. You must observe, track, alert, and react.
Want to learn more about how Databand can help you manage data pipelines? Request a demo from one of our experts.
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