The global big data market is forecasted to grow from $56 billion in 2020 to a whopping $103 billion by 2027. Across the board, enterprises are investing in technologies that help them process and leverage larger and larger data lakes.
However, at present, the ROI on these investments is mixed. In 2019 alone, losses attributed to cloud waste amounted to $14.1 billion. One major reason why overspending is extremely common is a lack of strategic optimization. At Pepperdata, internal data gathered from users prior to their onboarding confirms the scale of the waste and shows how autotuning can help.
The Cloud Waste Crisis
Every application, workload, service, or process, within any big data infrastructure, needs resources to run. However, when your organization is provisioning resources for workload peaks, there is an inherent component of cloud waste during non-peak times and non-peak segments of a given workflow.
Above is a table revealing how much cloud waste we see in big data applications prior to the use of Pepperdata. Across Spark and MapReduce, we see that an average of 63% of application usage is wasted.
Diving into our customer pre-onboarding data, we can see how this wastage is not limited to any particular industry or market vertical:
There are two strategies which must be deployed to address and mitigate the problem of cloud waste as a whole: Automation and Observability.
Autotuning is Imperative (Manual Won’t Cut It)
At scale, big data problems cannot be solved manually. They are simply too complex, fast moving, and contain too many factors to address in real time. Manual tuning won’t work. Teams need to leverage automation.
Traditionally, scheduling subsystems, cues, and resource pools have all been fairly static. Someone would take a look at the peak amount of work that needs to be done and allocate the appropriate resources. The advent of autoscaling provided an additional tool in the toolbox, but the levels of scaling are a whole server instance. The majority of the cloud waste is happening WITHIN the servers.
Automating the way in which workloads leverage server/instance resources is a required tactic when optimizing big data analytics stack performance to maximize your ROI. Pepperdata employs machine learning and intelligent scheduling to ensure service level agreements are met and applications have access to the right amount of resources throughout their life cycle.
Observability is Key
Observability ensures DevOps teams understand their distributed systems’ performance in a way that is actionable and comprehensive. With observability, teams can get the right performance data. They can stop being surprised by performance events, and they can figure out what’s slow, what’s broken, and what needs to be improved. This is key to cutting out cloud waste within applications and capacity management constructs like queues and resource pools.
Observability also allows all of the teams within an organization to leverage a common set of data to understand and evaluate performance throughout the stack. This synchronization is a time saver and serves to eliminate the finger pointing that can happen when different sources of performance allow for different interpretations of a given scenario. Having everyone on the same page, to begin with, is a key aspect of success in performance management.
How Pepperdata Can Help
Pepperdata provides enterprises with a suite of big data optimization solutions designed to boost your big data stack performance, cut down costs, and guarantee big data optimization from end to end.