Data Pipeline Observability: A Model For Data Engineers

We believe the world’s data pipelines need better data observability. But unfortunately, very little that happens in data engineering today is observable. Most data 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, you will often see a host of strange values and cryptic missing columns. Data engineers are scratching their heads and realizing that to correct, you must first observe.

“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 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 IBM® Databand®. “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.”

This proactive approach is particularly important when it comes to data pipelines.

Data pipeline observability refers to the ability to monitor and understand the state of a data pipeline at any point in time, especially with respect to its internal states, based on the system’s outputs. It goes beyond basic monitoring to provide a deeper understanding of how data is moving and being transformed in a pipeline, and is often associated with metrics, logging and tracing data pipelines.

Data pipelines often involve a series of stages where data is collected, transformed and stored. This might include processes like data extraction from different sources, data cleansing, data transformation (like aggregation) and loading the data into a database or a data warehouse. Each of these stages can have different behaviors and potential issues that can impact the data quality, reliability and overall performance of the system.

Observability provides insights into how each stage of the data pipeline functions, and how its inner workings correlate with specific types of outputs—especially outputs that do not provide the required levels of performance, quality or accuracy. These insights allow data engineering teams to understand what went wrong and fix it.

Data pipeline observability matters because pipelines have gone from complicated to complex—from many concurrent systems to many interdependent systems.

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 (link resides outside ibm.com) 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 USD 105 billion by 2027, according to Gartner (link resides outside ibm.com)—about the size of Ukraine’s economy. At this rate, corporate data volume is currently increasing 62% every month (link resides outside ibm.com). 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.

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 to monitor a DataOps pipeline, it rarely works out.

“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 cause errors and impact 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.

Data observability works with your data pipeline by providing insights into how your data flows and is processed from start to end. Here is a more detailed explanation of how data observability works within the data pipeline:

• Data ingestion: Observability begins from the point where data is ingested into the pipeline. You can monitor how much data is being ingested, how quickly it’s being processed, and whether there are any errors or delays.
• Data processing: As data moves through various stages of processing, observability tools can monitor the operation of each stage. This includes watching for failures, measuring latency, tracking resource usage and ensuring data is being transformed correctly.
• Data storage and delivery: Observability continues into the storage and delivery phase. It can monitor how quickly data is being written to the database or data warehouse, ensure data is being delivered to the correct destinations, and alert you to any issues.
• Error tracking and troubleshooting: Observability tools can help identify where errors occurred, their root causes, and even suggest remediation actions. This is critical for minimizing downtime and ensuring the reliability of your data pipeline.
• Performance optimization: By monitoring the performance of your data pipeline, observability tools can help identify bottlenecks and opportunities for optimization. This can lead to more efficient use of resources and faster processing times.
• Anomaly detection: Observability can help identify anomalies that could indicate potential issues or areas for improvement. For example, if data is taking significantly longer to process than usual, this could indicate a problem with a particular stage in the pipeline.
• Alerting and reporting: Observability tools often include alerting features that can notify you of potential issues in real-time, allowing for quick response. They also often provide comprehensive reporting features that can help you understand the overall health and performance of your data pipeline.

Data observability platforms provide insight that 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.

• 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. You need to know, not hope, that your fix addressed the root problem.

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 it do 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.

See how IBM Databand provides data pipeline monitoring to quickly detect data incidents like failed jobs and runs so you can handle pipeline growth. If you’re ready to take a deeper look, book a demo today.