The DataOps role is unique in analytics, aiming to enable data engineers, scientists, analysts, and governance to own the pipelines that run the assembly process. Essentially, DataOps engineers work on, but not in, these pipelines, according to a DataKitchen webinar titled ““
“We want to run our value pipeline like Toyota makes changes. We also want to be able to change that pipeline, take a piece of it, and iterate quickly and change our pipelines as fast companies do on their websites,” said Christopher Bergh, the CEO and “head chef” at DataKitchen.
The space of DataOps combines Agile development, DevOps, and statistical process controls and applies them to . However, the current challenges in organizations stem from the fact that people don’t all know that their job is to deliver value to the end user since they’re so focused on the immediate task at hand.
“The challenge is that in many ways, the DataOps role to many people that do isn’t there. It’s not apparent. So if they’re going to build something like a bunch of SQL or a new , they throw it to production and say, I’ve done my work. My definition of what I do as a data engineer is it worked for me. A lot of time, the challenges for people doing data analytics is they focus on their little part and think the process of putting it into production is someone else’s problem,” Bergh said. “It’s very task-focused and not value-focused. Done should mean it’s in production.”
DataOps engineering is about collaboration through shared abstraction, whether putting nuggets of code into pipelines, creating tests, running the factory, automating deployments, and of people in the organization. It’s then about automating many tasks. “DataOps engineering is about trying to take these invisible processes, pull them forward and make them visible through a shared abstraction and then automate them,” Bergh said.
Like many other scenarios, the challenge of automation is that no one owns the process. in. “While implementing a DataOps solution, we make sure that the to ensure data quality and to leave time for more innovation and reduce the stress as well as fear of failure,” said Charles Bloche, a data engineering director at DataKitchen.
Every error leads to a new that improves systems. The DataOps engineers also to catch more errors to recover faster and empower collaboration and reuse.
“For a data warehouse, the product is the dataset; for an analyst, the product is the analysis, and for a DataOps engineer, the product is an effective, repeatable process,” Bloche said. “We are less focused on the next deadline; we’re focused on
time. A DataOps engineer runs toward error because the error is the key to the feedback loop that makes complex processes reliable. Errors are data.”