Pitfalls of on-premise Hadoop spur rise of hosted Hadoop

Big Data is a rapidly growing market making inroads into numerous different businesses and industries all over the world. Companies are looking to make full use of data analytics through platforms like Hadoop to discover new strategies, gain more customer information, or create new products.

Data analytics, however, can be a challenge for some businesses when done on-premise. When doing on-premise Hadoop, businesses usually need to make big investments in infrastructure while also having the IT expertise on hand to make sure everything runs smoothly and efficiently. This in-house Hadoop expertise can be costly and often requires employees to handle large workloads.

On-premise data analytics also runs into scalability issues along with limitations on storage. Static clusters prevent jobs from benefiting from temporarily available nodes, and the foundation of many Hadoop deployments is not flexible or easily changed, leading to inefficient compute utilization.

Early adopters have also found that simply getting a cluster up and running and getting the machines to work seamlessly is challenging. Underestimating a workload quickly leads to processing bottlenecks that limit a company’s ability to focus on gathering actionable intelligence.

The problems surrounding on-premise Hadoop have led to increasing attention toward Hosted Hadoop. To learn more about the pitfalls of on-premise Hadoop and to learn more about Hadoop in the Cloud solutions, download the ebook “Big Data Belongs in the Cloud.”