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Taneja Blog / Big Data / Data Center Systems

Big Data Enterprise Maturity

It's time to look at big data again. Last week I was at Cloudera's growing and vibrant annual analyst event to hear the latest from the folks who know what's what. Then this week Strata (conference for data scientists) brings lots of public big data vendor announcements. A noticeable shift this year is less focus on how to apply big data and more about maturing enterprise features intended to ease wider data center level adoption. A good example is the "mixed big data workload QoS" cluster optimizating solution from Pepperdata.

For example, IT enterprise folks (and those with dev ops titles) with new big data infrastructure responsibilities still find a few holes and gaps to fill in production. One of the things we like about the Pepperdata team is that they started with a hard problem - QoS guarantees in big data scale-out clusters that enable realistic big data application SLA's - and are now easily expanding their uniquely positioned solution to provide some of those remaining enterprise management gaps. This time they are adding alerting to their dynamic (fully automated) optimization.

That first sounds like backwards motion - moving from automation back towards manual alert procedures. But all enterprises still need alerting for many reasons across everything in the data center. And sure, there are already lots of logfiles to mine for exceptions, and many layers of "things" that can be monitored with other tools. But Pepperdata now generates alerts that specfically relate to events that are impacting actual performance and service levels by leveraging its deep internal views (and ability to map to users, tasks, and jobs). And then they can alert on the remediating/tweaking/enforcement actions that it automatically takes so that an admin or app dev ops team can become aware of, correct, refine and optimize piggish (or starved) applications if necessary.

There are other key announcements rolling out in big data security, more kinds of analytics (graph analysis at scale is hot, as is dynamic/adaptive modeling techniques), secondary storage, data protection, data navigation/management and such that are easing enterprise adoption. Let us know what you think are the big remaining stumbling blocks to supporting production big data in your organization!

  • Premiered: 03/29/16
  • Author: Mike Matchett
Topic(s): Cloudera Pepperdata Big Data


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