Data Observability: Practices, Products and Frameworks You Need to Know

The emerging discipline of observability seeks to help business and IT leaders understand all the moving pieces. It monitors indicators, correlates them, identifies issues, assesses root causes, generates alerts, and remediates issues. But how do you extract just the right signals from all that noise? And how many tools do you need?

Data observability includes:

Data quality observability, which studies the quality and timeliness of data. It observes data in flight or at rest, for example by validating sample values and checking metadata such as value distributions and data volumes, schema, and lineage.
Data pipeline observability, which studies the quality and performance of data pipelines, including the infrastructure that supports them. It observes pipeline elements such as data sources, compute clusters, landing zones, targets, and applications by studying their logs, traces, and metrics.

Data observability contributes to three other types of observability. They are business observability, which studies business metrics and their trends, correlations, and anomalies; model observability, which studies the performance, accuracy, and compliance of ML or other analytics models; and operations observability, which studies the performance, availability, and utilization of applications and infrastructure.

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