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data storage systems

Results 1 - 25 of 74Sort Results By: Published Date | Title | Company Name
Published By: SDL     Published Date: Sep 20, 2019
Component Content Management: A New Paradigm in Intelligent Content Services While technology has changed the world, the way that companies manage information has inherently stayed the same. The advent of near-ubiquitous connectivity among applications and machines has resulted in a data deluge that will fundamentally alter the landscape of content management. From mobile devices to intelligent machines, the volume and sophistication of data have surpassed the ability of humans to manage it with outdated methods of collection, processing, storage, and analysis. The opportunity afforded by the advent of artificial intelligence (AI) has stimulated the market to search for a better way to capture, classify, and analyze this data in its journey to digital transformation (DX). The paradigm of document-based information management has proven to be a challenge in finding, reusing, protecting, and extracting value from data in real time. Legacy systems may struggle with fragmented information
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SDL
Published By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifi cally to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artificial intelligence within the Financial Services industry, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Infinidat EMEA     Published Date: May 14, 2019
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
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Infinidat EMEA
Published By: Infinidat EMEA     Published Date: May 14, 2019
Continuous data availability is a key business continuity requirement for storage systems. It ensures protection against downtime in case of serious incidents or disasters and enables recovery to an operational state within a reasonably short period. To ensure continuous availability, storage solutions need to meet resiliency, recovery, and contingency requirements outlined by the organization.
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Infinidat EMEA
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly, and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Oracle     Published Date: Jan 28, 2019
Databases tend to hold an organization’s most important information and power the most crucial applications. It only makes sense, then, to run them on a system that’s engineered specifically to optimize database infrastructure. Yet some companies continue to run their databases on do-it-yourself (DIY) infrastructure, using separate server, software, network, and storage systems. It’s a setup that increases risk, cost, complexity, and time spent deploying and managing the systems, given that it typically involves at least three different IT groups.
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Oracle
Published By: Dell EMC & Intel     Published Date: Dec 12, 2018
Business and IT leaders agree. IT Transformation is critical to compete in the digital economy. Drive innovation and agility, lower costs and speed deployment for real results. Modernise with leading hyper-converged, cloud, data storage, servers, open networking and data protection systems from Dell EMC powered by Intel ® .
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Dell EMC & Intel
Published By: Pure Storage     Published Date: Dec 05, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Read this MIT Technology Review custom paper to learn how advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics, helping to accelerate business outcomes for data driven organizations.
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Pure Storage
Published By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? Since data—structured and unstructured—can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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data, lake, amazon, web, services, aws
    
AWS
Published By: Dell EMC & Intel     Published Date: Sep 06, 2018
Datacenter improvements have thus far focused on cost reduction and point solutions. Server consolidation, cloud computing, virtualization, and the implementation of flash storage capabilities have all helped reduce server sprawl, along with associated staffing and facilities costs. Converged systems — which combine compute, storage, and networking into a single system — are particularly effective in enabling organizations to reduce operational and staff expenses. These software-defined systems require only limited human intervention. Code imbedded in the software configures hardware and automates many previously manual processes, thereby dramatically reducing instances of human error. Concurrently, these technologies have enabled businesses to make incremental improvements to customer engagement and service delivery processes and strategies.
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Dell EMC & Intel
Published By: Hewlett Packard Enterprise     Published Date: Aug 23, 2018
In an innovation-powered economy, ideas need to travel at the speed of thought. Yet even as our ability to communicate across companies and time zones grows rapidly, people remain frustrated by downtime and unanticipated delays across the increasingly complex grid of cloud-based infrastructure, data networks, storage systems, and servers that power our work.
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Hewlett Packard Enterprise
Published By: Amazon Web Services     Published Date: Jul 25, 2018
What is a Data Lake? Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. Download to find out more now.
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Amazon Web Services
Published By: Hewlett Packard Enterprise     Published Date: Jul 18, 2018
In an innovation-powered economy, ideas need to travel at the speed of thought. Yet even as our ability to communicate across companies and time zones grows rapidly, people remain frustrated by downtime and unanticipated delays across the increasingly complex grid of cloud-based infrastructure, data networks, storage systems, and servers that power our work.
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Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Jul 18, 2018
In an innovation-powered economy, ideas need to travel at the speed of thought. Yet even as our ability to communicate across companies and time zones grows rapidly, people remain frustrated by downtime and unanticipated delays across the increasingly complex grid of cloud-based infrastructure, data networks, storage systems, and servers that power our work.
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Hewlett Packard Enterprise
Published By: Dell PC Lifecycle     Published Date: May 15, 2018
Windows Server 2016 is an important release in enabling IT to deliver on the promise of the third platform. It provides a path to a seamless, integrated cloud environment—incorporating public, private and hybrid models—with the software-defined data center as the hub. In migrating to this next-generation data center model, it is essential that IT leaders choose the right partner for the compute platform, as well as storage, networking and systems management
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Dell PC Lifecycle
Published By: Hewlett Packard Enterprise     Published Date: Apr 20, 2018
In an innovation-powered economy, ideas need to travel at the speed of thought. Yet even as our ability to communicate across companies and time zones grows rapidly, people remain frustrated by downtime and unanticipated delays across the increasingly complex grid of cloud-based infrastructure, data networks, storage systems, and servers that power our work.
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Hewlett Packard Enterprise
Published By: Pure Storage     Published Date: Apr 18, 2018
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: IBM APAC     Published Date: Mar 19, 2018
Unstructured data has exploded in volume over the past decade. Unstructured data, media files and other data can be created just about anywhere on the planet using almost any smart device available today. As the amount of unstructured data grows exponentially, customers using this data need to be able to take advantage of the right storage solutions to support all of their file and object data requirements. IBM® recently added a new storage system to their Spectrum product family, IBM Spectrum Network Attached Storage (NAS). IBM Spectrum NAS adds another software-defined file storage system to IBM’s current unstructured data storage solutions, IBM Spectrum Scale™ and IBM Cloud Object Storage (COS). Below, we will discuss the three systems and supply some guidance on when and where to use each of them.
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IBM APAC
Published By: SAS     Published Date: Mar 06, 2018
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of scenario, and companies like Ford expect to have production vehicles in the latter part of 2020. Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems. Other reasons for the increased momentum include: maturing capabilities with methods and algorithms refactored to run in memory; the
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SAS
Published By: Cloudian     Published Date: Feb 21, 2018
We are critically aware of the growth in stored data volumes putting pressure on IT budgets and services delivery. Burgeoning volumes of unstructured data commonly drive this ongoing trend. However, growth in database data can be expected as well as enterprises capture and analyze data from the myriad of wireless devices that are now being connected to the Internet. As a result, stored data growth will accelerate. Object-based storage systems are now available that demonstrate these characteristics. While they have a diverse set of use cases, we see several vendors now positioning them as on-premises targets for backups. In addition, integration of object-based data protection storage with cloud storage resources is seen by these vendors as a key enabler of performance at scale, cost savings, and administrative efficiency.
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Cloudian
Published By: Cloudian     Published Date: Feb 15, 2018
We are critically aware of the growth in stored data volumes putting pressure on IT budgets and services delivery. Burgeoning volumes of unstructured data commonly drive this ongoing trend. However, growth in database data can be expected as well as enterprises capture and analyze data from the myriad of wireless devices that are now being connected to the Internet. As a result, stored data growth will accelerate. Object-based storage systems are now available that demonstrate these characteristics. While they have a diverse set of use cases, we see several vendors now positioning them as on-premises targets for backups. In addition, integration of object-based data protection storage with cloud storage resources is seen by these vendors as a key enabler of performance at scale, cost savings, and administrative efficiency.
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Cloudian
Published By: Pure Storage     Published Date: Jan 12, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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data, deep learning, automated, intelligence, pure storage
    
Pure Storage
Published By: AWS     Published Date: Nov 02, 2017
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it’s readily available to be categorized, processed, analyzed, and consumed by diverse groups within an organization. Since data - structured and unstructured - can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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AWS
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