Controlling cloud spend is like flying blindfolded

March 9, 2020

Here’s how to take off the blindfold.

Most modern enterprises are managing a hybrid—and usually multi-cloud—big data environment. Faced with this architecture challenge, many IT-operations teams are struggling to control spend. They are increasingly pressured as hybrid big data environments grow in size and complexity. 

Why is it that cloud spend is so hard to control?

Cloud complexity is a visibility nightmare

IT teams are running enormous big data clusters with thousands of nodes, and they are tasked with optimizing application performance, supporting SLAs, uncovering infrastructure inefficiencies, and minimizing MTTR (mean time to repair). They need to deal with any malfunctioning workload as quickly as humanly possible. But they are struggling to get enough insight into their systems to act as swiftly and as effectively as is required.

Budgets need to get back in line 

Pepperdata's Ash Munshi

Bills are often rolled up and sent to finance departments, but the developers and ITOps teams never actually get much insight into how their actions translate into cloud spend. Suddenly, capacity is limitless, and computing teams are greedy. Approaches such as chargeback models can help manage cloud spend. But on their own they’re not enough.

The answer? Full stack visibility.

To manage spend, teams need to right-size workloads. And the key to right-sizing is in visibility. You need to determine usage patterns, understand average peak computing demand, map storage patterns, determine the number of core processors required, treat nonproduction and virtualized workloads with care. 

To meet budgets, organizations need transparency, so that the people who generate the cost are aware of what they’re generating. 

The key to both rightsizing and budgeting clarity is in full stack visibility. 

To stay right-sized, you need full insight into the CPU, memory and storage constituting every instance. Users need to know what is actually going on with their big data jobs. They need the data and insights that will give them a clear picture of spend, wastage, and fluctuations in resources. 

With these insights, they can begin to bring budgets back in line.

However, the data and insights that ITOps teams need are almost impossible to acquire without the right tool. The key to reducing the runaway costs of a hybrid big data architecture is in efficiently analyzing and right-sizing on-prem and matching cloud resources more closely to utilization. The visibility that can empower an organization to do this can only come from dedicated software that offers powerful insights into jobs and usage from a bird’s-eye view. Not job by job, or individual user by individual user, but across the whole infrastructure. 

Ultimately, in the quest to control cloud spend, analytics are key. Without powerful, in-depth insights, big data teams simply don’t have the information they need to do their job. For this, a dedicated solution is required—one that can:

  • Visualize and optimize big data operations instantly at scale 
  • Offer a single dashboard for all big data environments 
  • Cover on-prem and in the cloud
  • Initiate automated infrastructure optimization

Ash Munshi is the CEO of Pepperdata.