When monitoring cloud resources, there are several factors to consider.

DBS Bank Head of Automation, Infrastructure for DBS Big Data, AI and Analytics Luis Carlos Cruz Huertas has a 1-on-1 discussion with Unravel CEO and Co-founder Kunal Agarwal about the convergence of DataOps and FinOps. The discussion, Leading Cultural Change for Data Efficiency, Agility, and Cost Optimization, was held at a recent Untap event in New York and revolves around best practices, lessons learned, and insights on.

In the first three articles in this four-post series, my colleague Jason English and I explored DataOps observability, the connection between DevOps and DataOps, and data-centric FinOps best practices. In this concluding article in the series, I’ll explore DataOps resiliency – not simply how to prevent data-related problems, but also how to recover from them quickly, ideally without impacting the business and its customers.

Machine learning (ML) enables organizations to extract more value from their data than ever before. Companies who successfully deploy ML models into production are able to leverage that data value at a faster pace than ever before. But deploying ML models requires a number of key steps, each fraught with challenges.

Most organizations spend at least 37% (sometimes over 50%) more than they need to on their cloud data workloads. A lot of costs are incurred down at the individual job level, and this is usually where there’s the biggest chunk of overspending. Two of the biggest culprits are oversized resources and inefficient code. But for an organization running 10,000s or 100,000s of jobs, finding and fixing bad code or right-sizing resources is shoveling sand against the tide.

IT and data executives find themselves in a quandary about deciding how to wrangle an exponentially increasing volume of data to support their business requirements – without breaking an increasingly finite IT budget. Like an overeager diner at a buffet who’s already loaded their plate with the cheap carbs of potatoes and noodles before they reach the protein-packed entrees, they need to survey all of the data options on the menu before formulating their plans for this trip.

By Jason Bloomberg, President, Intellyx 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 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.