Check out our Big Data Cloud Technology Report for the full story.
The cloud is an exciting, paradigm-shifting technology. It is loaded with both promise and potential. From reducing operational costs to streamlining business processes to innovating product cycles, moving to the cloud is an attractive prospect to most modern organizations.
However, adopting big data in the cloud is not without its challenges. Post-migration issues plague even the most stable and sound of enterprises. In many cases, these challenges have resulted in subpar migration outcomes—even failures.
We surveyed 750 business enterprises to determine how they deployed their big data workloads in the cloud, the challenges they met, and the solutions they sought.
Why Enterprises Move to the Cloud
Cloud computing has become a key pillar to business growth and success, indicated by the big surge of enterprises moving their critical processes and applications from an on-premise model to the cloud. By the end of 2022, over 90% of business organizations around the world are expected to have adopted big data cloud technologies and implemented hybrid cloud strategies.
Mike Cisek, VP Analyst at Gartner, points out that a major driver of cloud adoption is to enable digital transformation while reducing spend. Cloud computing reduces, if not eliminates, the costs associated with hardware upkeep and maintenance.
Among the crucial processes enterprises migrate to the cloud is big data. Organizations store, manage, and analyze their big data in the cloud, mainly because of the cloud’s capacity to handle mountains of raw information from disparate sources.
This is why scalability ranks among the top drivers of adoption. Enterprises can scale up their cloud infrastructure to keep their big data processes running at an optimal pace. Big data stacks automatically expand or compress their compute resources based on their real-time requirements, ensuring workloads are running smoothly.
Cloud computing drives business agility through taking control of disparate business systems, unifying operational silos, and automating time-intensive processes. With big data cloud processes in place, enterprises can rapidly adapt to new processes in the most cost-effective way without hurting their quality.
These are some of the most trumpeted benefits of big data technologies in the cloud. However, moving to the cloud and leveraging big data can be trickier than it seems.
The Challenges of Big Data Operations in the Cloud
While most enterprises expect to see their operational costs substantially reduced after migrating to the cloud, many receive a shock. Their cloud spending has actually exceeded their initial budget.
If cloud computing is meant to drive down costs, how come 80% of businesses are overshooting their cloud allocation?
A major driver for the increased cloud spending is the shift from CapEx to OpEx. In a CapEx model, budgets are easy to determine and assign. In contrast, the OpEx model is trickier as operational requirements shift, making it difficult for enterprises to define their spend. Infinite cloud resources and the absence of a cloud-based spending governance framework make it easy for organizations to overspend.
Autoscaling has been advertised as a means to reduce cloud spend by ensuring big data stacks in the cloud have adequate resources when traffic spikes up. The overall benefit of autoscaling is the removal of manual intervention to supply additional compute resources whenever traffic surges.
However, default autoscaling configurations set by cloud providers like AWS can result in more cloud spend. In most cases, cloud infrastructures overprovision compute resources based on peak-level requirements. While this method guarantees that there are compute resources available once traffic intensifies, it also means that a significant number of resources have gone to waste during events when traffic is lower than anticipated.
Without visibility into their big data stacks in the cloud, enterprises find it hard to fully optimize their IT infrastructure, quickly identify issues, troubleshoot problems, and reduce downtimes. Overall infrastructure performance suffers as a result, leading to their inability to meet their SLAs.
What We Learned from Our Big Data Cloud Survey
We conducted a detailed survey exploring how enterprises are running and managing their big data workloads in the cloud. Here are some key findings:
- 64% of enterprises are highly concerned about “cost management and containment.”
- 1 in 3 respondents predict their cloud-related expenses to be 20% to 40% higher than initial budget.
- 1 in 12 enterprises said they will likely spend 40% more than their initial allocation
- For the majority of enterprises, the foremost priority of their big data cloud initiative is to “better optimize current cloud resources”.
In short, overspend is a major challenge. How can enterprises optimize their cloud operations so that costs are brought under control?
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The Pepperdata Impact
Pepperdata provides enterprises with a comprehensive platform that grants them absolute visibility into and control of their big data stack in the cloud. This empowers them to completely and effectively manage their compute resource consumption and reduce cloud spend.
Through automation and machine learning Pepperdata analyzes your cloud infrastructure, workloads, and offers recommendations to help you ensure that performance and costs are optimized.
With Pepperdata automated performance and cost optimization, enterprises now have a means to be in full control over their cloud spend. The Pepperdata suite provides users with observability and visibility, allowing them to detect and resolve issues quickly—without causing extensive downtime.
In addition, users gain insight for planning, debugging, and troubleshooting, plus the confidence that their applications will meet SLAs.
Check out our Big Data Cloud Technology Report for more information.