Items Tagged: big+data+lake
New approaches to scalable storage
With all these scalable storage approaches, IT organizations must evaluate the options against their data storage and analytics needs, as well as future architectures.
- Premiered: 03/16/15
- Author: Mike Matchett
- Published: TechTarget: Search Data Center
Navigate data lakes to manage big data
While the data lake concept appeals to business today, IT administrators must exercise caution prior to a full-scale implementation.
- Premiered: 06/05/15
- Author: Mike Matchett
- Published: TechTarget: Search Storage
Big data analytics applications impact storage systems
Analytics applications for big data have placed extensive demands on storage systems, which Mike Matchett says often requires new or modified storage structures.
- Premiered: 09/03/15
- Author: Mike Matchett
- Published: TechTarget: Search Storage
Enterprise Storage that Simply Runs and Runs: Infinidat Infinibox Delivers Incredible New Standard
Storage should be the most reliable thing in the data center, not the least. What data centers today need is enterprise storage that affordably delivers at least 7-9's of reliability, at scale. That's a goal of less than three seconds of anticipated unavailability per year - less than the reliability of most data centers.
Data availability is the key attribute enterprises need most to maximize their enterprise storage value, especially as data volumes grow into scales. Yet traditional enterprise storage solutions aren’t keeping pace with the growing need for greater than the oft-touted 5-9’s of storage reliability, instead deferring to layered on methods like additional replication copies, that can drive up latency and cost, or settling for cold tiering which zaps performance and reduces accessibility.
Within the array, as stored data volumes ramp up and disk capacities increase, RAID and related volume/LUN schemes begin to fall down due to longer and longer disk rebuild times that create large windows of vulnerability to unrecoverable data loss. Other vulnerabilities can arise from poor (or at best, default) array designs, software issues, and well-intentioned but often fatal human management and administration. Any new storage solution has to address all of these potential vulnerabilities.
In this report we will look at what we mean by 7-9’s exactly, and what’s really needed to provide 7-9’s of availability for storage. We’ll then examine how Infinidat in particular is delivering on that demanding requirement for those enterprises that require cost-effective enterprise storage at scale.
Now Big Data Works for Every Enterprise: Pepperdata Adds Missing Performance QoS to Hadoop
While a few well-publicized web 2.0 companies are taking great advantage of foundational big data solution that they have themselves created (e.g. Hadoop), most traditional enterprise IT shops are still thinking about how to practically deploy their first business-impacting big data applications – or have dived in and are now struggling mightily to effectively manage a large Hadoop cluster in the middle of their production data center. This has led to the common perception that realistic big data business value may yet be just out of reach for most organizations – especially those that need to run lean and mean on both staffing and resources.
This new big data ecosystem consists of scale-out platforms, cutting-edge open source solutions, and massive storage that is inherently difficult for traditional IT shops to optimally manage in production – especially with still evolving ecosystem management capabilities. In addition, most organizations need to run large clusters supporting multiple users and applications to control both capital and operational costs. Yet there are no native ways to guarantee, control, or even gain visibility into workload-level performance within Hadoop. Even if there wasn’t a real high-end skills and deep expertise gap for most, there still isn’t any practical way that additional experts could tweak and tune mixed Hadoop workload environments to meet production performance SLA’s.
At the same time, the competitive game of mining of value from big data has moved from day-long batch ELT/ETL jobs feeding downstream BI systems, to more user interactive queries and business process “real time” applications. Live performance matters as much now in big data as it does in any other data center solution. Ensuring multi-tenant workload performance within Hadoop is why Pepperdata, a cluster performance optimization solution, is critical to the success of enterprise big data initiatives.
In this report we’ll look deeper into today’s Hadoop deployment challenges and learn how performance optimization capabilities are not only necessary for big data success in enterprise production environments, but can open up new opportunities to mine additional business value. We’ll look at Pepperdata’s unique performance solution that enables successful Hadoop adoption for the common enterprise. We’ll also examine how it inherently provides deep visibility and reporting into who is doing what/when for troubleshooting, chargeback and other management needs. Because Pepperdata’s function is essential and unique, not to mention its compelling net value, it should be a checklist item in any data center Hadoop implementation.
To read this full report please click here.
Galactic Exchange can get your Hadoop cluster up and running in just 5 minutes
Stealthy startup Galactic Exchange Inc. burst out of the shadows this weekend touting a new product that’s able to spin up an Hadoop or Spark cluster, ready to go, in just five minutes.
- Premiered: 03/28/16
- Author: Taneja Group
- Published: Silicon Angle
Secondary data storage: A massively scalable transformation
Capitalize on flash with interactive, online secondary data storage architectures that make a lot more data available for business while maximizing flash investment.
- Premiered: 10/06/17
- Author: Mike Matchett
- Published: TechTarget: Search Storage