As the famous term of DevOps appeared in the tech field earlier than DataOps, the latter is commonly mistreated as an application of DevOps practice to data analytics.
This informative yet easy to digest article, will shed the light on the differences between the two terms and their corresponding practices within the organizations.
DevOps stands for (DEVelopment OPerationS) is the gold standard approach and practice to accelerate software development build lifecycles, and it has been widely adopted by almost every software company that considers Agile as their backbone project management approach. Yet this approach is well defined, and is continuing to deliver a great value to its adopters, through reducing software releasing cycles from months to almost seconds, it has been considered to serve only software and code releases and cycle times, and this approach cannot be applied for the management of the wide range of data analytics releases and value creation spectrum.
From this need to optimize not only software releases, but the end-to-end range of data analytics value creation revolving not only around software releases, emerged the DataOps approach which is considered as a rather different evolution to the DevOps approach. Since its inception, DataOps combined three majors data and software management best-practices, which are Agile, DevOps, and lean manufacturing in a seek to reduce the full life-cycle of data analytics from ideation to the tangible creation of value through graphs and models leveraging the power of the three best-practices approaches.
In everis, we are specialized in both proven methodologies to empower organizations to create value through their data of all shapes. Reach out for us to learn more about our experiences in implementing these best practices for our clients.
One common misconception about DataOps is that it is just DevOps applied to data analytics