Data Fabric with DataOps
To support one of the key principles of a Data Fabric architecture, data are assets that evolve into Data Products, Data Fabric needs to be married with DataOps principles that focus on quality control and efficiencies in the development and delivery of data. DataOps applies principles in how data and data pipelines are developed, such as with embedded automation and orchestration. It expects the testing of data, assumptions, and integrations at a grand scale. It establishes quality controls from initial data transport from the source to the destination(s), and then after that, executes ongoing data monitoring. Data is fluid; the business evolves and requirements change, which requires proactive monitoring and analysis to ensure the successful delivery of data to consumers.
The DataOps phases are iterative and are not always executed in sequence. Sometimes, a phase may be executed multiple times. For example, during testing, you might find data validation...
