Much of the additional complexity involved with DataOps is wrapped around two components:
Data Sandbox Management involves defining the data sources, tools, services, and vendors are to be used for a DataOps project. It’s similar to how developers set up their own isolated environments based upon project specifications for selecting software languages, libraries, tools, and services. But where developers tend to use a few dozen components, DataOps may need to synthesize hundreds of data sources using a wide variety of tools and services.
Data Orchestration is the automated process of bringing all of this data together from so many different sources and making it more useful so people can use it. This would be impossible without automation to address the challenges associated with the Voluminous V’s of Big Data (Volume, Velocity, Variety, et al).
These sources often involve third-party APIs, but could also entail a variety of platforms, periodicals, and reports using a very wide range of electronic file formats. Not all DataOps projects are so verbose – but data collection efforts tend to expand over time.