by Angela Guess
A recent press release reports, “Unravel Data, provider of a full-stack performance intelligence platform for optimizing Big Data operations (DataOps1), today emerged from stealth, announcing the first-ever platform to accelerate Big Data applications, optimize resource usage, and provide operations intelligence, all from a single platform. Addressing the larger Big Data market, which analysts at IDC estimate will be worth $187 billion by 20192, Unravel Data helps DataOps teams, which includes data scientists, BI analysts, IT operations and systems architects, resolve performance and reliability issues with Big Data applications running on clusters scaling up to several thousand nodes. With a click of a button, the Unravel platform provides DataOps teams with actionable information to accelerate time-to-value from their Big Data applications.”
The release continues, “Until now, visibility into the entire Big Data application stack has not been available — a situation made dire by the fact that problems with an application could result from anywhere in the stack. For example, from bad code, inefficient data partitioning, mismatched system configuration settings, best-guessed resource allocation, or infrastructure issues. Making matters worse, a Big Data stack running multiple applications must deal with resource contention and prioritization issues that can reduce both individual application and overall system performance. Amidst these mounting challenges, no solutions have emerged for making analytics-based recommendations in plain English to improve individual application and full-stack performance. Adding to the challenge, industry analysts have highlighted the acute shortage of talent for running and maintaining Big Data systems. Speaking of Hadoop alone, Gartner estimates that, ‘through 2018, 70 percent of Hadoop deployments will fail to meet cost savings and revenue generation objectives due to shortage of skills and integration challenges’.”
Read more at Marketwired.
Photo credit: Unravel Data