DevOps was started more than a decade ago as a movement, not a product or solution category. DevOps offered us a way of collaborating between development and operations teams, using automation and optimization practices to continually accelerate the release of code, measure everything, lower costs, and improve the quality of application delivery to meet customer needs.
As organizations run more data applications and pipelines in the cloud, they look for ways to avoid the hidden costs of cloud adoption and migration. Teams seek to maximize business results through cost visibility, forecast accuracy, and financial predictability. Watch the breakout session video from Data Teams Summit and see how organizations apply agile and lean principles using the FinOps framework to boost efficiency, productivity, and innovation. Transcript available below.
As DataOps moves along the maturity curve, many organizations are deciphering how to best balance the success of running critical jobs with optimized time and cost governance. Watch the fireside chat from Data Teams Summit where Mark Sear, Head of Data Platform Optimization for Maersk, shares how his team is driving towards enabling strong engineering practices, design tenets, and culture at one of the largest shipping and logistics companies in the world.
Uncontrolled cloud costs pose an enormous risk for any organization. The longer these costs go ungoverned, the greater your risk. Volatile, unforeseen expenses eat into profits. Budgets become unstable. Waste and inefficiency go unchecked. Making strategic decisions becomes difficult, if not impossible. Uncertainty reigns.
This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.
Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.
