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.
DevOps is a way of building and running software in which development and operations work as one team. Automation, shared ownership and fast feedback let that team ship changes often and safely.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
Data engineering is the discipline of building and running the systems that collect, store, move and prepare data. Its output is reliable data that analysts, data scientists and product features can use.
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.
A data pipeline is an automated series of steps that moves data from where it is created to where it is stored, analysed or used. Steps along the way may extract, clean, reshape or enrich it.
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