DataOps is an emerging methodology for developing and deploying data analytics solutions. Adapted from the DevOps and agile techniques for software development, DataOps takes a holistic approach to the people, processes, and technology required to build and automate data pipelines. It has four key pillars: continuous integration and deployment (CI/CD), orchestration, testing, and monitoring.

- Joe Hilleary in DataOps Deep Dive: Different Approaches to the DataOps Platform

December 1, 2020 (Report)

DataOps is an engineering methodology and set of practices designed for rapid, reliable, and repeatable delivery of production-ready data and operations-ready analytics and data science models. DataOps enhances and advanced governance through engineering disciplines that support versioning of data, data transformations, data lineage, and analytic models. DataOps supports business operational agility with the ability to meet new and changing data and analysis needs quickly. It also supports portability and technical operations agility with the ability to rapidly redeploy data pipelines and analytic models across multiple platforms in on-premises, cloud, multi-cloud, and hybrid ecosystems.

- Dave Wells in DataOps: More Than DevOps for Data Pipelines

July 4, 2019 (Blog)

DataOps is an emerging set of practices, processes, and technologies for building and enhancing data and analytics pipelines… DataOps builds on concepts popular in the software engineering field, such as agile, lean, and continuous integration/continuous delivery, but addresses the unique needs of data and analytics environments, including the use of multiple data sources and varied use cases that range from data warehousing to data science. It relies heavily on test automation, code repositories, collaboration tools, orchestration frameworks, and workflow automation to accelerate delivery times while minimizing defects.

- Wayne Eckerson in DataOps: Industrializing Data and Analytics

June 30, 2018 (Report)