What is…
Three terms this industry uses loosely, explained properly. No vendor pitch, no jargon tax. Start with DataOps if any of them is new to you: the other two are parts of it, not substitutes for it.
The discipline
What Is DataOps?
Running data and analytics the way software teams run code: tested changes, fast releases, measured results.
Read the explainer Manifesto The DataOps Manifesto The whole idea in 18 principles, plus one we added: start with data testing. More than 30,000 data and analytics professionals have signed it. Read the 18 principles → The data halfWhat Is DataOps Data Quality?
Quality as a production control instead of a project: profile the data, generate the tests, act on what breaks.
Read the explainer Manifesto The DataOps Quality Manifesto Data quality in 24 principles: quality comes only from testing, no manual checks, and automated tests at the source, at ingestion, in production, and in development. Read the 24 principles → The process halfWhat Is DataOps Process Observability?
Watching the process that makes the data: did every step run, in order, on time, with its tests green.
Read the explainer Manifesto The Data Journey Manifesto Process observability in 22 principles: know what should be, find the problem before your customer does, and treat data production as a factory. Read the 22 principles →Once the words make sense
The explainers cover what these things are. The free courses cover how to do them, with the module list and the exercises.