Data observability is continuous monitoring of data pipelines and datasets so that problems are caught before users notice. It watches freshness, volume, schema, distribution and lineage.
Data quality is how well data fits its intended use, judged on qualities such as accuracy, completeness, consistency, timeliness, validity and uniqueness. Teams track it with automated checks, for example flagging duplicate customer records or missing order dates before they reach a finance report.
DataOps is a set of practices that applies DevOps and agile ideas to data work. Automation, testing, version control and monitoring help teams deliver data products quickly and reliably.
Observability is the ability to understand what a running system is doing, and why, from the data it emits. That data is mainly logs, metrics and traces, which let engineers answer new questions without shipping new code.
Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
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