Hadoop is great. Without it, big data processing would be a whole lot harder. However, manually tuning Hadoop is an inefficient process that reduces job performance by 30-50%.

As analytics platforms grow in scale and complexity, both on-prem and in the cloud, managing and maintaining efficiency is a critical challenge. But many organizations are wasting money with their dated approach to Hadoop. 

On December 4th, Pepperdata Field Engineer Eric Lotter hosted a webinar in which he discussed how Pepperdata can help an organization:

  • Maximize infrastructure investment
  • Achieve up to 50 percent increase in throughput, and run more jobs on existing infrastructure
  • Ensure cluster stability and efficiency
  • Avoid overspending on unnecessary hardware
  • Spend less time in backlog queues

On a typical cluster, hundreds and even thousands of decisions are made per second, increasing typical enterprise cluster throughput up to 50 percent. Even the most experienced operator dedicated to resource management can’t make manual configuration changes with the required precision and speed. 

Furthermore, it can often be very difficult even for experienced operators to find root causes when problems arise. Then, there’s the issue of inefficient cluster utilization. Clusters have a lot of headroom, and the more you set up different clusters for different workloads, the more capacity you waste.

Check out Eric’s webinar to learn how to automatically tune and optimize cluster resources, and recapture wasted capacity. Eric provides relevant use case examples, and shows how any enterprise can get more out of their infrastructure investment.