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Items Tagged: IBM+Spectrum

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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
news / Blog

IBM Back in the Growth Game with Cleversafe Acquisition

On October 5, 2015, IBM announced the acquisition of Cleversafe, a provider of object-based storage software.

news

Scale-out architecture and new data protection capabilities in 2016

What are the next big things for the data center in 2016? Applications will pilot the course to better data protection and demand more resources from scale-out architecture.

  • Premiered: 02/17/16
  • Author: Mike Matchett
  • Published: TechTarget: Search Data Center
Topic(s): TBA scale-out TBA Data protection TBA DP TBA scale-out architecture TBA analysis TBA Data Center TBA data lake TBA Hadoop TBA hadoop cluster TBA cluster TBA Backup TBA Talena TBA HPE TBA 3PAR TBA flat backup TBA Snapshot TBA Snapshots TBA StoreOnce TBA Oracle TBA Oracle ZDLRA TBA ZDLRA TBA Zero Data Loss Recovery Appliance TBA converged TBA convergence TBA Cloud TBA backup server TBA Virtualization TBA Storage TBA Big Data TBA Lustre
Profiles/Reports

Got mid-sized workloads? Storwize family to the rescue

Myths prevail in the IT industry just as they do in every other facet of life. One common myth is that mid-sized workloads, exemplified by smaller versions of mission critical applications, are only to be found in mid-size companies. The reality is mid-sized workloads exist in businesses of all sizes. Another common fallacy is that small and mid-size enterprises (SMEs) or departments within large organizations and Remote/Branch Offices (ROBOs) have lesser storage requirements than their larger enterprise counterparts. The reality is companies and groups of every size have business-critical applications and these workloads require enterprise-grade storage solutions that offer high-performance, reliability and strong security. The only difference is IT groups managing mid-sized workloads frequently have significant budget constraints. This is a tough combination and presents a big challenge for storage vendors striving to satisfy mid-sized workload needs.

A recent survey conducted by Taneja Group showed mid-size and enterprise needs for high-performance storage were best met by highly virtualized systems that minimize disruption to their current environment. Storage virtualization is key because it abstracts away all the differences of various storage boxes to create 1) a single virtualized storage pool 2) a common set of data services and 3) a common interface to manage storage resources. These storage virtualization capabilities are beneficial to the overall enterprise storage market and they are especially attractive to mid-sized storage customers because storage virtualization is the core underlying capability that drives efficiency and affordability.

The combination of affordability, manageability and enterprise-grade functionality is the core strength of the IBM Storwize family built upon IBM Spectrum Virtualize, the quintessential virtualization software that has been hardened for over a decade with IBM SAN Volume Controller (SVC). Simply stated – few enterprise storage solutions match IBM Storwize’s ability to deliver enterprise-grade functionality at such a low cost. From storage virtualization and auto-tiering to real-time data compression and proven reliability, Storwize with Spectrum Virtualize offers an end-to-end storage footprint and centralized management that delivers highly efficient storage for mid-sized workloads, regardless of whether they exist in small or large companies.

In this paper, we will look at the key requirements for mid-sized storage and we will evaluate IBM Storwize with Spectrum Virtualize’s ability to tackle mid-sized workload requirements. We will also present an overview of IBM Storwize family and provide a comparison of the various models in the Storwize portfolio.

Publish date: 06/24/16
news

Conquer unstructured data: Scale-out NAS vs. object storage

Learn how scale-out NAS and object storage systems work, their pros and cons, manageability and how they integrate with your storage infrastructure.

  • Premiered: 08/04/16
  • Author: Jeff Kato
  • Published: TechTarget: Search Storage
Topic(s): TBA scale-out TBA scale-out NAS TBA Storage TBA object storage TBA storage infrastructure TBA NAS TBA cloud object storage TBA Cloud Storage TBA Cloud TBA Public Cloud TBA scalability TBA scalable TBA Metadata TBA Portable Operating System Interface TBA POSIX TBA fsync TBA real-time data TBA EMC TBA EMC Isilon TBA Isilon TBA IBM TBA IBM Spectrum TBA IBM Spectrum Scale TBA Qumulo TBA Scality TBA global scalability TBA Amazon TBA reliability TBA erasure coding TBA Dropbox
news

IBM flash storage expands with midrange all-flash products

IBM's all-flash portfolio grows, with a new midrange and entry-level option, and the vendor adds a migration program aimed at EMC-Dell customers.

  • Premiered: 08/23/16
  • Author: Taneja Group
  • Published: TechTarget: Search Solid State Storage
Topic(s): TBA Arun Taneja TBA IBM TBA EMC TBA Dell TBA EMCDell TBA all-flash TBA All Flash TBA Migration TBA Storwize TBA FlashSystem TBA Cloud TBA hyperscale TBA Flash TBA SSD TBA Hybrid TBA IBM Spectrum TBA Virtualization TBA EMC VMAX TBA VMAX TBA XtremeIO TBA DSSD TBA NetApp TBA Microsoft NetApp TBA HPE TBA Pure Storage
news

IBM hybrid storage play acknowledges enterprise realities

The recent IBM hybrid storage push aims to court enterprise clients with resources on premises and in the public cloud - and offer something AWS doesn't.

  • Premiered: 11/03/16
  • Author: Taneja Group
  • Published: TechTarget: Search Cloud Computing
Topic(s): TBA IBM TBA hybrid storage TBA Storage TBA Amazon TBA Amazon AWS TBA Cloud Storage TBA all-flash TBA All Flash TBA Block Storage TBA Cleversafe TBA cloud object storage TBA replication TBA Amazon S3 TBA AWS TBA S3 TBA Panzura TBA Google TBA Microsoft TBA Public Cloud TBA IBM Spectrum TBA Virtualization TBA Jeff Kato TBA IaaS TBA SoftLayer TBA PaaS
news

Hyper-convergence: It's for more than primary data storage

The lines between primary and secondary storage and applications such as hyper-convergence remain blurry. But they are a starting point for further discussion.

news

A cure for secondary data storage services blues

Effective user-centric data storage management eases copy data management and object storage uptake while enabling secondary storage services to evolve.

  • Premiered: 03/07/17
  • Author: Steve Ricketts
  • Published: TechTarget: Search Storage
Topic(s): TBA secondary data TBA secondary storage TBA Storage TBA copy data management TBA CDM TBA cloud object storage TBA object storage TBA Data protection TBA Backup TBA Disaster Recovery TBA DR TBA Archiving TBA Compression TBA Migration TBA SLA TBA resiliency TBA software-defined TBA software-defined storage TBA SDS TBA compliance TBA Dell EMC TBA flash storage TBA SSD TBA scalability TBA Cloud TBA Cohesity TBA scale-out TBA hyperconverged TBA hyper-converged TBA hyperconvergence
news / Blog

IBM Cloud Object Storage Provides a Strong Data Foundation for Modern Healthcare

The healthcare industry is facing tremendous data challenges as the medical community evolves to support rapid growth in digital medical imaging, new government regulations and improvements in patient care.

Profiles/Reports

IBM Cloud Object Storage Provides the Scale and Integration Needed for Modern Genomics Infra.

For hospitals and medical research institutes, the ability to interpret genomics data and identify relevant therapies is key to provide better patient care through personalized medicine. Many such organizations are racing forward, analyzing patients’ genomic profiles to match more clinically actionable treatments using artificial intelligence (AI).

These rapid advancements in genomic research and personalized medicine are very exciting, but they are creating enormous data challenges for healthcare and life sciences organizations. High-throughput DNA sequencing machines can now process a human genome in a matter of hours at a cost approaching one thousand dollars. This is a huge drop from a cost of ten million dollars ten years ago and means the decline in genome sequencing cost has outpaced Moore’s Law (see chart). The result is an explosion in genomic data – driving the need for solutions that can affordably and securely store, access, share, analyze and archive enormous amounts of data in a timely manner.

Challenges include moving large volumes of genomic data from cost-effective archival storage to low latency storage for analysis to reduce the time needed to analyze genetic data. Currently, it takes days to do a comprehensive DNA sequence analysis.

Sharing and interpreting vast amounts of unstructured data to find relationships between a patient’s genetic characteristics and potential therapies adds another layer of complexity. Determining connections requires evaluating data across numerous unstructured data sources, such as genomic sequencing data, medical articles, drug information and clinical trial data from multiple sources.

Unfortunately, the traditional file storage within most medical organizations doesn’t meet the needs of modern genomics. These systems can’t accommodate massive amounts of unstructured data and they don’t support both data archival and high-performance compute. They also don’t facilitate broad collaboration. Today, organizations require a new approach to genomics storage, one that enables:

  • Scalable and convenient cloud storage to accommodate rapid unstructured data growth
  • Seamless integration between affordable unstructured data storage, low latency storage, high performance compute, big data analytics and a cognitive healthcare platform to quickly analyze and find relationships among complex life science data types
  • A multi-tenant hybrid cloud to share and collaborate on sensitive patient data and findings
  • Privacy and protection to support regulatory compliance

Publish date: 03/22/17
Profiles/Reports

Cloud Object Storage for the Healthcare Data Blues

The healthcare industry continues to face tremendous cost challenges. The U.S. government estimates national health expenditures in the United States accounted for $3.2 trillion last year – nearly 18% of the country’s total GDP. There are many factors that drive up the cost of healthcare, such as the cost of new drug development and hospital readmissions. In addition, there’s compelling studies that show medical organizations will need to evolve their IT environment to curb healthcare costs and improve patient care in new ways, such as cloud-based healthcare models aimed at research community collaboration, coordinated care and remote healthcare delivery.

For example, Goldman Sachs recently predicted that the digital revolution can save $300 billion in spending in the healthcare sector by powering new patient options, such as home-based patient monitoring and patient self-management. Moreover, the most significant progress may come from a medical organization transforming their healthcare data infrastructure. Here’s why:

  • Advancements in digital medical imaging has resulted in an explosion of data that sits in  picture archiving and communications systems (PACS) and vendor neutral archives (VNAs).
  • Patient care initiatives such as personalized medicine and genomics require storing, sharing and analyzing massive amounts of unstructured data.
  • Regulations such as the Health Insur­ance Portability and Accountability Act (HIPAA) require organizations to have policies for long term image retention and business continuity.

Unfortunately, traditional file storage approaches aren’t well-suited to manage vast amounts of unstructured data and present several barriers to modernizing healthcare infrastructure. A recent Taneja Group survey found the top three challenges to be:

  • Lack of flexibility: Traditional file storage appliances require dedicated hardware and don’t offer tight integration with collaborative cloud storage environments.
  • Poor utilization: Traditional file storage requires too much storage capacity for system fault tolerance, which reduces usable storage.
  • Inability to scale: Traditional storage solutions such as RAID-based arrays are gated by controllers and simply aren’t designed to easily expand to petabyte storage levels.

As a result, healthcare organizations are moving to object storage solutions that offer an architecture inherently designed for web scale storage environments. Specifically, object storage offers healthcare organizations the following advantages:

  • Simplified management, hardware independence and a choice of deployment options – private, public or hybrid cloud – lowers operational and hardware storage costs
  • Web-scale storage platform provides scale as needed and enables a pay as you go model
  • Efficient fault tolerance protects against site failures, node failures and multiple disk failures
  • Built in security protects against digital and physical breeches

Publish date: 03/22/17
news / Blog

IBM Spectrum Protect Plus Simplifies Data Protection for Virtual Environments

Modern organizations want simple data protection that is easily accessible to VM and application administrators. This fact was highlighted in a recent Taneja Group Survey, where over 40% of participants indicated “simple to manage” is the most critical data protection factor when selecting a data protection solution.

  • Premiered: 08/23/17
  • Author: Steve Ricketts
Topic(s): IBM simplicity VM Virtual Machine Data protection SLA Storage Backup API IBM Spectrum Steve Ricketts