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What is DataOps?
DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. Research firm Gartner further describes the methodology as one focused on “improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.”
DataOps goals
According to Dataversity, the goal of DataOps is to streamline the design, development, and maintenance of applications based on data and data analytics. It seeks to improve the way data are managed and products are created, and to coordinate these improvements with the goals of the business. According to Gartner, DataOps also aims “to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”
DataOps vs. DevOps
DevOps is a software development methodology that brings continuous delivery to the systems development lifecycle by combining development teams and operations teams into a single unit responsible for a product or service. DataOps builds on that concept by adding data specialists — data analysts, data developers, data engineers, and/or data scientists — to focus on the collaborative development of data flows and the continuous use of data across the organization.
DataKitchen, which specializes in DataOps observability and automation software, maintains that DataOps is not simply “DevOps for data.” While both practices aim to accelerate the development of software (software that leverages analytics in the case of DataOps), DataOps has to simultaneously manage data operations.
DataOps principles
Like DevOps, DataOps takes its cues from the agile methodology. The approach values continuous delivery of analytic...