We have organizations that feel the need of the accessing & governing that data that is supplied in the analytics engine & machine learning. And DataOps is always rising in such organizations. Just like DevOps, DataOps is also about providing something for production, in the case of DevOps it’s code, in the case of DataOps it’s Data. In the recent years, organizations asked fewer questions about what DataOps is, because the general definition of the data is quite understood. However, the less obvious questions must have been the business value it delivers, especially if organizations are approaching DataOps as another technology to implement.
Today, we have a lot of tools, systems, and technologies available but the real challenge lies in finding the set that exactly matches your organizational needs. Another important point is getting the right people; organizations will have to create a data-first mindset and then get the right people on board. One important thing to note here is that DataOps don’t just ensure the availability of the data, it also ensures timely delivery of the data, and that it’s properly governed. The DataOps teams must focus on optimizing & simplifying current data pipelines & put their people, design, architecture, patterns, and best practices into the data development efforts.