DataOps

DataOps applies the practices that transformed software delivery, version control, automated testing, continuous integration and deployment, monitoring, to the development and operation of data pipelines. Rather than manually running scripts and eyeballing output, a DataOps team tests transformations before they ship, deploys pipeline changes through the same kind of review and release process used for application code, and treats a broken pipeline as an incident to be tracked and resolved.

It differs from plain workflow orchestration, which schedules and runs pipelines, by adding the surrounding discipline: automated data quality checks that fail a build the way a unit test fails one, environments for testing changes before production, and change management so a schema edit doesn't silently break every downstream consumer. It is closely related to MLOps, which applies the same philosophy specifically to machine learning model pipelines rather than general data pipelines.

DataOps matters because data pipelines fail in ways that are easy to miss, a job can run "successfully" and still produce wrong numbers, and data observability and automated tests are what catch that before a stakeholder does. The main pitfall is adopting DataOps tooling, CI pipelines, testing frameworks, without the cultural change of actually treating data incidents with the same urgency as an application outage, which leaves the tooling in place but the underlying problems unaddressed.