Data analytics teams challenged by inflexibility and poor quality have found that DataOps can address these and many other obstacles. DataOps includes tools and process improvements that enable faster, more responsive data analytics while maintaining a high level of quality and reliability.
Data analytic teams can implement DataOps in seven simple steps: (1) Add data and logic tests, (2) Use a version control system, (3) Branch and merge,(4) Use multiple environments, (5) Reuse and containerize, (6) Parameterize your processing and (7) Work Without Fear™.
