Since the term was coined, DataOps has expanded the way that people think about data analytics teams and their potential. 2020 was a huge year in DataOps industry acceptance. Media mentions of DataOps are on track to increase 52% over the prior year. To date in 2020,...
Implementing DataOps requires a combination of new methods and automation that augment an enterprise’s existing toolchain. The fastest and most effective way to realize the benefits of DataOps is to adopt an off-the-shelf DataOps Platform. Some organizations try to...
Almost every data analytic tool can be used in DataOps, but some don’t enable the full breadth of DataOps benefits. DataOps views data-analytics pipelines like a manufacturing process that can be represented by directed acyclic graphs. Each node in the graph...
In a previous blog, we talked about aligning technical environments to facilitate the migration of analytics from development to production. In this post, we will introduce the concept of “Kitchens” and illustrate how they simplify the deployment of data analytics. In...
Production engineers and analytics developers sometimes row the enterprise boat in different directions. Perhaps it is their roles and objectives which keep them at odds. It’s the production engineer’s duties to keep data operations up and running. Innovation is good,...
DataOps improves your ability to orchestrate your data pipelines, automate testing and monitoring, and speed new feature deployment. DataOps recognizes that, in any data project, many different tools play an important role as independent components of the data...
DataOps places great emphasis on productivity. Team velocity can improve significantly when the data organization uses tools and methods that promote component reuse. Architecting analytics as microservices is one way to stimulate code reuse. The challenge is that the...
Breaking Through the Noise A Guide to Understanding DataOps Solutions DataOps is the hot topic on every data professional’s lips these days, and we expect to hear much more about DataOps in 2020. This is not surprising given that DataOps holds true potential for...
Leading software companies perform millions of code releases per year. Typical data-analytics organizations perform less than 10. This gap explains why most data-analytics projects fail to deliver. Without the capability to move at lightning speed, data analytics...
20 Predictions for a DataOps 2020: 20 Articles Predicting DataOps is the Next Big Thing in 2020 Here at DataKitchen, we’ve been promoting the ideas behind DataOps for many years. It is great to see so many influential organizations like Gartner, Eckerson, TDWI, and...
