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Items Tagged: big+data+lake

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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
Topic(s): TBA Mike Matchett TBA TechTarget TBA Storage TBA scalable TBA scalability TBA analytics TBA Data Storage TBA Big Data TBA Block Storage TBA File Storage TBA object storage TBA scale-out TBA scale-up TBA Performance TBA Capacity TBA HA TBA high availability TBA latency TBA IOPS TBA Flash TBA SSD TBA File System TBA Security TBA NetApp TBA Data ONTAP TBA ONTAP TBA EMC TBA Isilon TBA OneFS TBA Cloud
news

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
Topic(s): TBA data lake TBA Storage TBA Big Data TBA storage infrastructure TBA Data protection TBA big data lake TBA analysis TBA HDFS TBA Hadoop TBA Hadoop virtualization TBA Virtualization TBA Hadoop Distributed File System TBA software-defined TBA software-defined storage TBA BI TBA Business Intelligence TBA Disaster Recovery TBA Business Continuity TBA BC TBA DR TBA analytics TBA Spark TBA HP TBA Vertica TBA HP Haven TBA Haven TBA OLAP TBA data-aware
news

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
Topic(s): TBA Mike Matchett TBA Big Data TBA analytics TBA Storage TBA Primary Storage TBA scalability TBA Business Intelligence TBA BI TBA AWS TBA Amazon AWS TBA S3 TBA HPC TBA High Performance Computing TBA High Performance TBA ETL TBA HP Haven TBA HP TBA Hadoop TBA Vertica TBA convergence TBA converged TBA IOPS TBA Capacity TBA latency TBA scale-out TBA software-defined TBA software-defined storage TBA SDS TBA YARN TBA Spark
Profiles/Reports

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.

Publish date: 09/29/15
Profiles/Reports

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.

Publish date: 12/17/15
news

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
Topic(s): TBA Galactic Exchange TBA Hadoop TBA hadoop cluster TBA cluster TBA Spark TBA ClusterGX TBA strata TBA simplicity TBA Infrastructure TBA Big Data TBA Mike Matchett TBA Apache TBA Apache Mesos TBA Docker TBA hyperconverged TBA hyperconvergence TBA application performance TBA Backup TBA flexibility TBA Cloud TBA Storage TBA analysis TBA data lake TBA big data lake TBA IoT
news

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
Topic(s): TBA secondary data TBA secondary storage TBA Storage TBA scalable TBA scalability TBA SSD TBA Flash TBA Mike Matchett TBA Primary Storage TBA Datacenter TBA Data Center TBA Datrium TBA Big Data TBA Backup TBA Data protection TBA Snapshot TBA Snapshots TBA Archiving TBA Archive TBA Apple TBA second-tier TBA Time Machine TBA convergence TBA Virtualization TBA erasure coding TBA Metadata TBA high availability TBA Capacity TBA Cohesity TBA Hedvig