What is a common challenge customers face with Big Data workloads on-premises?

One of the most prevalent challenges that customers experience with Big Data workloads on-premises is the tracking of growth patterns and scaling infrastructure. As organizations accumulate vast amounts of data, they need to ensure that their infrastructure can support increasing data volumes and processing requirements. This includes not only the hardware capabilities to handle larger datasets but also the ability to adapt to changing workloads over time, which may involve scaling up (adding more power to existing machines) or scaling out (adding more machines).

On-premises environments often require significant manual intervention to effectively monitor and manage these growth patterns, making it labor-intensive. Additionally, as data needs evolve, transitioning to a more scalable architecture may necessitate substantial upfront investment and planning, further complicating the growth process. Thus, scaling infrastructure in response to rapidly changing Big Data workloads represents a major hurdle in an on-premises setting.