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Items Tagged: IBM+Spectrum+Scale

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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
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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
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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