data architecture

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By: Panasas     Published Date: Oct 02, 2014
HPC and technical computing environments require the collection, storage,and transmission of large-scale datasets. To meet these demands, datacenter architects must consider how increasing storage capacity over time will affect HPC workloads, performance, and system availability. While many enterprises have looked to scale-up NAS to meet their storage needs, this approach can lead to data islands that make it difficult to share data. Distributed, scale-out storage was developed to get around the technology limitations of scale-up NAS architectures.
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     Panasas
By: Data Direct Networks     Published Date: Dec 31, 2015
Using high performance parallel storage solutions, geologists and researchers can now incorporate larger data sets and execute more seismic and reservoir simulations faster than ever before, enabling higher fidelity geological analysis and significantly reduced exploration risk. With high costs of exploration, oil and gas companies are increasingly turning to high performance DDN storage solutions to eliminate I/O bottlenecks, minimize risk and costs, while delivering a larger number of higher fidelity simulations in same time as traditional storage architectures.
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     Data Direct Networks
By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IBM Cloud Private for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Private (ICP). The latter is intended to a) provide all the benefits of cloud computing but inside your firewall and b) provide a stepping-stone, should you want one, to broader (public) cloud deployments. Further, ICP has a micro-services architecture, which has additional benefits, which we will discuss. Going beyond this, ICP for Data itself is intended to provide an environment that will make it easier to implement datadriven processes and operations and, more particularly, to support both the development of AI and machine learning capabilities, and their deployment. This last point is important because there can easily be a disconnect Executive summary between data scientists (who often work for business departments) and the people (usually IT) who need to operationalise the work of those data scientists
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     Group M_IBM Q2'19
By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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     Group M_IBM Q2'19
By: Forcepoint     Published Date: May 14, 2019
IDC Infobrief: How Security Solutions Enable Cloud Success Cloud applications have transformed how organizations conduct business, increasing productivity and reducing costs. However, moving to the cloud means critical data now flows outside the organization’s traditional security boundaries. Unsurprisingly, security concerns still rank as the number one reason for not using cloud services*. This IDC InfoBrief, sponsored by Forcepoint, outlines five key security considerations and best practices for safe cloud adoption, including: Multi-cloud adoption: exposing challenges for security architectures Cloud-first and GDPR implications Cloud application visibility and control Data management and DLP in the cloud Leveraging User and Entity Behavior Analytics (UEBA) for protection Read the eBook to find out the five information security lessons learned from transitioning to the cloud. *IDC’s 2017 CloudView Survey; IDC’s 2017 CloudImpact Survey
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     Forcepoint
By: Forcepoint     Published Date: May 14, 2019
IDC Infobrief: How Security Solutions Enable Cloud Success Cloud applications have transformed how organizations conduct business, increasing productivity and reducing costs. However, moving to the cloud means critical data now flows outside the organization’s traditional security boundaries. Unsurprisingly, security concerns still rank as the number one reason for not using cloud services*. This IDC InfoBrief, sponsored by Forcepoint, outlines five key security considerations and best practices for safe cloud adoption, including: Multi-cloud adoption: exposing challenges for security architectures Cloud-first and GDPR implications Cloud application visibility and control Data management and DLP in the cloud Leveraging User and Entity Behavior Analytics (UEBA) for protection Read the eBook to find out the five information security lessons learned from transitioning to the cloud. *IDC’s 2017 CloudView Survey; IDC’s 2017 CloudImpact Survey
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     Forcepoint
By: Blue Prism     Published Date: Mar 28, 2019
Robotic process automation describes the use of technology to automate tasks that are traditionally done by a human being. The technology itself mimics an end user by simulating user actions such as navigating within an application or entering data into forms according to a set of rules. RPA is often used to automate routine administrative tasks that typically require a human being to interact with multiple systems, but RPA technology is evolving to support the automation of increasingly sophisticated processes at scale within enterprise architectures rather than on the desktop. Over the past two years, RPA has been adopted by a number of business process outsourcing (BPO) providers and a growing number of end-user organizations are now deploying the technology themselves to create “virtual workforces” of robotic workers.
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     Blue Prism
By: Cisco EMEA     Published Date: Nov 13, 2017
The HX Data Platform uses a self-healing architecture that implements data replication for high availability, remediates hardware failures, and alerts your IT administrators so that problems can be resolved quickly and your business can continue to operate. Space-efficient, pointerbased snapshots facilitate backup operations, and native replication supports cross-site protection. Data-at-rest encryption protects data from security risks and threats. Integration with leading enterprise backup systems allows you to extend your preferred data protection tools to your hyperconverged environment.
Tags : hyperflex, systems, data platform, storage efficiency, business, cisco
     Cisco EMEA
By: Hewlett Packard Enterprise     Published Date: May 10, 2019
Nimble Secondary Flash array represents a new type of data storage, designed to maximize both capacity and performance. By adding high-performance flash storage to a capacity-optimized architecture, it provides a unique backup platform that lets you put your backup data to work. Nimble Secondary Flash array uses flash performance to provide both near-instant backup and recovery from any primary storage system. It is a single device for backup, disaster recovery, and even local archiving. By using flash, you can accomplish real work such as dev/test, QA, and analytics. Deep integration with Veeam’s leading backup software simplifies data lifecycle management and provides a path to cloud archiving.
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     Hewlett Packard Enterprise
By: SAP     Published Date: May 18, 2014
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
Download this whitepaper to learn the results of this latest exploration of the emerging world of in-memory database technologies.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools, it management
     SAP
By: SAP     Published Date: May 18, 2014
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: SAP     Published Date: May 18, 2014
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
     SAP
By: HPE Intel     Published Date: Jan 11, 2016
A famous architect once said that the origin of architecture was defined by the first time “two bricks were put together well.” And the more bricks you have, the more important putting them together well becomes. The same holds true in our data centers. The architecture of our compute, storage and network devices has always been important, but as the demands on our IT infrastructures grow, and we add more “bricks,” the architecture becomes more critical.
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     HPE Intel
By: Hewlett Packard Enterprise     Published Date: Mar 26, 2018
Today’s data centers are expected to deploy, manage, and report on different tiers of business applications, databases, virtual workloads, home directories, and file sharing simultaneously. They also need to co-locate multiple systems while sharing power and energy. This is true for large as well as small environments. The trend in modern IT is to consolidate as much as possible to minimize cost and maximize efficiency of data centers and branch offices. HPE 3PAR StoreServ is highly efficient, flash-optimized storage engineered for the true convergence of block, file, and object access to help consolidate diverse workloads efficiently. HPE 3PAR OS and converged controllers incorporate multiprotocol support into the heart of the system architecture
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     Hewlett Packard Enterprise
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