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The DataOps approach seeks to apply the principles of agile software development and DevOps (combining development and operations) to data analytics, to break down silos and promote efficient, streamlined data handling across many segments. DataOps is served by tools, technologies and techniques that combine multiple stages of a staged process to improve and enhance the management of data for enterprise use.
Many different types of frameworks can facilitate a DataOps approach. The use of Apache Oozie to handle Apache Hadoop projects could be called DataOps, so could the use of ETL processes in a streamlined data flow. In general, DataOps replaces a “waterfall” or sequential strategy for analytics with one that involves “hand-holding” across teams and departments: For example, a universal agreement on semantics of data and metadata is a step on the road to applied DataOps. This idea was really only implemented in 2015 and later, and some experts see 2017 as ushering in more of a focus on DataOps for enterprise IT and data analytics.