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Data Terms Glossary

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Data Observability

Data observability is the capability and process of being on top of your data pipelines at a level that provides the ability to catch, identify and resolve issues in a timely manner.

Why do you need data observability?

As data users come to see data analytics as ready-to-consume products, data teams and engineers are expected to make sure that their data product is working at all times. That includes being accessible, functional and providing accurate data that can be applied immediately. 

Since the data pipelines that feed the end data analytics product are as susceptible to errors as any product manufacturing line, data engineers need data observability tools to stay on top of their pipelines and catch errors or malfunctions in near real-time, preferably before the problem reaches the end consumer.

The later a data issue is caught, the bigger and longer the clean-up job will be. So data observability saves time and effort for data teams by enabling them to catch issues as early as possible.

What capabilities does data observability include?

Data observability includes the following capabilities:

  • Detection and establishment of historical trends and baselines
  • Monitoring of pipeline speed and efficiency
  • Detection of suspicious anomalies in the freshness, distribution or volume of incoming or outgoing data
  • Detection of leading indicators of known data processing or accuracy issues
  • Setting checkpoints for intermediate stages of a data pipeline
  • Alerts for suspicious anomalies, leading indicators or failure to reach checkpoints
  • Automated remediation for specified issues
  • Data lineage to track the root cause of the problem upstream and the impact of the problem downstream

Comprehensive data observability platforms will include most or all of the above capabilities. Data observability open source tools will usually be restricted to one or a handful of the above capabilities, necessitating connecting them in order to create a more complete data observability architecture.

What are the circumstances in which data pipeline observability is especially important?

Data pipeline observability is particularly important in fast-moving cloud-native development environments. The speed of a cloud-native environment is a tremendous asset, but it can become a liability if it is errors or malfunctions being distributed at high speeds. Data observability tools enable data teams to keep their oversight moving at the same pace as data product development, deployment, and distributions.

Additionally, data observability increases in importance in environments that rely heavily on external data sources. If you don’t have control over the quality of the data itself, it becomes critical to be able to watch it and its impact closely.

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