BrainBlog for Unravel Data by Jason Bloomberg
Part 2 of the Demystifying Data Observability Series for Unravel Data
In part one of this series, fellow Intellyx analyst Jason English explained the differences between DevOps and DataOps, drilling down into the importance of DataOps observability.
The question he left open for this article: how did we get here? How did DevOps evolve to what it is today, and what parallels or differences can we find in the growth of DataOps?
DevOps Precursors
The traditional, pre-cloud approach to building custom software in large organizations separated the application development (‘dev’) teams from the IT operations (‘ops’) personnel responsible for running software in the corporate production environment.
In between these two teams, organizations would implement a plethora of processes and gates to ensure the quality of the code and that it would work properly in production before handing it to the ops folks to deploy and manage.
Such ‘throw it over the wall’ processes were slow and laborious, leading to deployment cycles many months long. The importance of having software that worked properly, so the reasoning went, was sufficient reason for such onerous delays.
Then came the Web. And the cloud. And enterprise digital transformation initiatives. All of these macro-trends forced enterprises to rethink their plodding software lifecycles.
Not only were they too slow to deliver increasingly important software capabilities, but business requirements would evolve far too quickly for the deployed software to keep up.
Such pressures led to the rise of agile software methodologies, cloud native computing, and DevOps.
Finding the Essence of DevOps
The original vision of DevOps was to pull together the dev and ops teams to foster greater collaboration, in hopes that software deployment cadences would accelerate while maintaining or improving the quality of the resulting software.
Over time, this ‘kumbaya’ vision of seamless collaboration itself evolved. Today, we can distill the essence of modern DevOps into these five elements:
- A cultural and organizational shift away from the ‘throw it over the wall’ mentality to greater collaboration across the software lifecycle
- A well-integrated, comprehensive automation suite that supports CI/CD activities, along with specialists who manage and configure such technologies, i.e., DevOps engineers
- A proactive, shift-left mentality that seeks to represent production behavior declaratively early in the lifecycle for better quality control and rapid deployment
- Full-lifecycle observability that shifts problem resolution to the left via better prediction of problematic behavior and preemptive mitigation of issues in production
- Lean practices to deliver value and improve efficiency throughout the software development lifecycle
Furthermore, DevOps doesn’t live in a vacuum. Rather, it is consistent with and supports other modern software best practices, including infrastructure-as-code, GitOps, and the ‘cattle not pets’ approach to supporting the production environment via metadata representations that drive deployment.
Read the entire BrainBlog here.
