• Understand what a data pipeline is.
• Understand what data observability is.
• Learn three metrics used to measure observability and scout for data issues.
You would be hard-pressed to find an industry today that doesn’t leverage data in some manner. In fact, data has become an integral part of day-to-day life for many businesses, helping to facilitate more insightful decision-making, assist in streamlining internal processes, and much more.
With that said, the volume of data being collected has grown significantly over the last decade. And the pipelines built to process this data have become considerably more intricate. These factors make the need for visibility into data workflows more important than ever. It allows organizations to verify that data is being processed properly, thereby ensuring that the output being provided is complete and accurate.
Data observability can assist in providing this increased visibility, enabling organizations to better ensure the quality of the data driving key decisions. Keep reading for a more in-depth discussion of this topic.
Before touching on data observability and how to make data pipelines observable, it’s important to define what we mean by the term “data pipeline.” A data pipeline is a set of processes for collecting data (often from a variety of sources), performing various operations upon this data to transform it appropriately (cleaning, standardizing, filtering, etc.), and then delivering the processed data to a destination for analysis. Data typically flows through a data pipeline in batches or as a continuous stream.
In the context of software, the concept of observability has become popular in recent years. In large part, it involves pairing monitoring tasks with additional context to provide teams with the knowledge that a problem exists (at the earliest possible moment) and the insights necessary to quickly evaluate why the problem is occuring. The idea is to enable the detection, analysis, and resolution of problems in a time-efficient manner — reducing MTTA, MTTR, and overall system downtime.
Data observability takes the above concept and applies it to data pipelines and their output. Data pipelines can be subject to a variety of problems that can be difficult to recognize and even more difficult to evaluate. This includes performance problems and issues impacting data quality. The aim of data observability is to help teams identify the existence of problems within these pipelines, then pair this information with the detail that’s necessary to fully understand the issue and efficiently reach a resolution. In doing so, data downtime can be reduced and the organization can enjoy an increased level of confidence and comfort with data that is very likely backing key business processes.
As is true with any system, data pipelines will experience issues from time to time. And as mentioned earlier, these can include issues with processing such as high levels of latency or lower than anticipated throughput, as well as errors in processing that result in data quality issues characterized by incomplete or unusable output.
Observability that provides insight into pipeline performance and output makes it possible for organizations to limit data downtime. Below, we will discuss a few metrics to track and tactics to employ to gain the advantages of observability.
Key dataset characteristics should be monitored to help identify issues with data quality at the earliest possible moment in time. If the data backing critical reports hasn’t been updated in the necessary timeframe, for instance, then an issue could exist somewhere within the flow that is preventing updated output from reaching its destination. Missing data can also be indicative of problems. If the volume of data populating tables is much lower than anticipated, this could be cause for concern and should be investigated. For example, maybe a recent change to the flow is causing data to be processed in an incorrect manner.
In short, keeping tabs on the properties of the data itself can give teams the insights they need to learn of problems within their pipelines in instances where it isn’t otherwise obvious.
Key pipeline performance metrics such as latency and throughput should also be monitored. Furthermore, these metrics should be alerted upon when they fail to reflect expected values. By tracking latency and throughput metrics at various stages within a data pipeline, responders can quickly narrow the search for a performance bottleneck when things go wrong. In effect, response personnel can use this information to efficiently isolate the problematic portion of a pipeline, thereby facilitating the performance of root cause analysis in a focused manner. Ultimately, this leads to faster time to resolution.
Data lineage can also serve to provide critical visibility into data processing. This includes information about where the data came from, how the data has been transformed, and why it has moved through the workflow in the way that it has. By empowering teams with these insights, it becomes easier for the organization to validate the output of their data pipelines. Moreover, lineage can be used in troubleshooting. When issues are encountered, data lineage can provide responders with the mechanism to analyze how the data traveled through the pipeline to get into its current state.
With data playing such an important role for organizations in every industry, avoiding data downtime is key. Implementing data observability can enable organizations to better maintain data pipelines that produce accurate and usable output. This can be done in part by taking a few important steps to enable incident responders to learn of data issues in a timely manner and perform focused root cause analysis that yields quick and effective resolutions.