data architecture

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Published By: Systemware     Published Date: Sep 07, 2018
Five key trends are shaping the enterprise content management (ECM) space. New business objectives, new market imperatives, and new cloud-based platforms compel technology and business decision makers to take a fresh look at their strategy and underlying platforms. This report will help enterprise architecture (EA) professionals make informed decisions about their content management agenda.
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systemware, content management, digital transformation, content cloud, top key trends, intelligent information, data storage, enterprise content management, it management, content services, enterprise architecture
    
Systemware
Published By: Systemware     Published Date: Sep 07, 2018
How’s your digital transformation going? What processes are you focusing on? Who’s leading the transformation? What technologies are a priority? What challenges do you face? To answer these questions, tap into the Forrester Data Global Business Technographics® Business And Technology Services Survey of your peers. Digital business pros should open this infographic to see the state of digital transformation.
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systemware, content management, digital transformation, content cloud, top key trends, intelligent information, data storage, enterprise content management, it management, content services, enterprise architecture
    
Systemware
Published By: Intel     Published Date: Sep 27, 2019
The stakes are high in today's data centers. Organisations have access to massive quantities of data promising valuable insights and new opportunities for business. But data center architects need to rethink and redesign their system architectures to ingest, store and process all that information. Similarly, application owners need to assess how they can process data more effectively. Those who don't re-architect might find themselves scrambling just to keep from being drowned in a data deluge.
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Intel
Published By: Intel     Published Date: Sep 30, 2019
Mountains of data promise valuable insights and innovation for businesses that rethink and redesign their system architectures. But companies that don’t re-architect might find themselves scrambling just to keep from being buried in the avalanche of data. The problem is not just in storing raw data, though. For businesses to stay competitive, they need to quickly and cost-effectively access and process all that data for business insights, research, artificial intelligence (AI), and other uses. Both memory and storage are required to enable this level of processing, and companies struggle to balance high costs against limited capacities and performance constraints. The challenge is even more daunting because different types of memory and storage are required for different workloads. Furthermore, multiple technologies might be used together to achieve the optimal tradeoff in cost versus performance. Intel is addressing these challenges with new memory and storage technologies that emp
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Intel
Published By: Group M_IBM Q119     Published Date: Mar 04, 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 Q119
Published 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
Published 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
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
In our 29-criteria evaluation of machine learning data catalogs (MLDCs) providers, we identified the 12 most significant ones — Alation, Cambridge Semantics, Cloudera, Collibra, Hortonworks, IBM, Infogix, Informatica, Oracle, Reltio, Unifi Software, and Waterline Data — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise architecture (EA) professionals make the right choice.
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Group M_IBM Q2'19
Published By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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Domino Data Lab
Published By: Oracle + Dyn     Published Date: Jun 27, 2017
"Global DNS performance and availability are critical to business continuity, security and end-user experience. With online applications, content, data and services often scattered across the internet and housed in your own DCs, your CDN or in your cloud instances; it’s more important than ever to have a robust, redundant DNS solution making sure your assets get delivered as quickly and reliably as possible. This whitepaper reviews the business advantages of implementing a high availability DNS architecture using redundant DNS services. You will learn: • The critical role DNS plays in the user experience • The failure and outage risks of relying on a single DNS solution • The major resiliency and performance differences between unicast vs anycast addressing • Criteria for evaluating a managed DNS service provider Download Now! "
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Oracle + Dyn
Published By: Datastax     Published Date: Oct 21, 2019
The majority of enterprises using cloud will be living in a hybrid deployment world for the foreseeable future. Data and analytics leaders must understand the risks and benefits in using the primary scenarios for hybrid cloud DBMS, and how they align with core use cases and architectures. Read this Gartner report to learn more about how you can evolve your data management strategy to reflect the latest trends and technologies.
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Datastax
Published By: MarkLogic     Published Date: Nov 07, 2017
Business demands a single view of data, and IT strains to cobble together data from multiple data stores to present that view. Multi-model databases, however, can help you integrate data from multiple sources and formats in a simplified way. This eBook explains how organizations use multi-model databases to reduce complexity, save money, lessen risk, and shorten time to value, and includes practical examples. Read this eBook to discover how to: Get unified views across disparate data models and formats within a single database Learn how multi-model databases leverage the inherent structure of data being stored Load as is and harmonize unstructured and semi-structured data Provide agility in data access and delivery through APIs, interfaces, and indexes Learn how to scale a multi-model database, and provide ACID capabilities and security Examine how a multi-model database would fit into your existing architecture
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MarkLogic
Published By: Schneider Electric     Published Date: Feb 17, 2017
In this white paper, we will look into: • The changing face of the colocation buyer • Industry structure, including mergers and acquisitions • The Internet of Things and big data • Edge computing • Cloud computing and Internet Giants • The impact of data center infrastructure management (DCIM) • Data center design architectures
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colocation, iot, internet of things, big data, edge computing, cloud computing, dcim, data center infrastructure management, data center design architectures
    
Schneider Electric
Published By: Schneider Electric     Published Date: Feb 17, 2017
This white paper provides a quantitative TCO analysis of the two architectures, and illustrates the key drivers of both the capex and opex savings of the improved architecture.
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data center, power, cooling, data center architecture, power and cooling infrastructure, tco, total cost of ownership
    
Schneider Electric
Published By: SAS     Published Date: May 24, 2018
Implementing omnichannel marketing is often difficult, especially when using digital channels. Most products designed for digital channels lack the ability to integrate with traditional channels that have on-site processes and data. SAS Customer Intelligence 360 enables businesses to use both cloud and on-site channels and data. This integration is possible because of SAS software’s hybrid cloud architecture.
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SAS
Published By: HP - Intel® Xeon® processors     Published Date: Nov 07, 2012
In this white paper, HP and Veeam Software outline an end-to-end storage and data protection architecture optimized for VMware vSphere and Microsoft® Hyper-V environments.
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virtualization, converged infrastructure, cloud computing, convergence, mission-critical applications, servers, cloud, it infrastructure, data management, business technology, data center
    
HP - Intel® Xeon® processors
Published By: GoodData     Published Date: Sep 17, 2013
While some IT best practices are applicable to BI, many are not. BI is different. Specifi cations are oft en outdated by the time they are collected. Business users demand more control; therefore, IT standards oft en take a back seat. Enterprises that understand and embrace these differences are oft en the most successful with BI.
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gooddata, business intelligence, data. better data, bigger ideas, bi reference architecture, bi platform, software development
    
GoodData
Published By: FireEye     Published Date: Feb 28, 2014
Organizations face a new breed of cyber attacks that easily thwart traditional defenses. These advanced attacks are targeted. They are persistent. And they are devastatingly effective at breaching your systems and stealing your sensitive data. This paper examines: The limitations of existing security solutions; Several security architectures, including sandbox-based products; An architecture built from the ground up to truly protect against today's advanced attacks.
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fireeye, persistent threats, advanced attacks, data centers, cyber-attacks, speed of detection, accuracy of detection, small businesses, information security, security threats, fireeye platform, protecting data, cyber targets, cybercriminals, prime target, midsize businesses, security, it management
    
FireEye
Published By: MarkLogic     Published Date: Mar 29, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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enterprise, metadata, management, organizations, technology, tools, mark logic
    
MarkLogic
Published By: MarkLogic     Published Date: May 07, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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agile, enterprise, metadata, management, organization
    
MarkLogic
Published By: MobileIron     Published Date: Feb 14, 2019
What if you could protect your company from data loss before a mobile attack occurs? The reality is that mobile threats are everywhere and the risks are escalating. But in the world of modern work, how can IT protect mobile devices while allowing users easy access to corporate data on a device of their choice anytime, anywhere? Watch this webinar on demand to learn how mobile threat defense is reducing risks amongst some of the largest mobility programs in the world. We will reveal: -How mobile attacks are targeting corporate data, bypassing traditional IT defenses -Why threat detection and remediation protects your data, without disrupting user productivity -Why mobile threat detection is an essential component of a layered defense architecture With one app, MobileIron Threat Defense delivers unparalleled mobile threat detection and remediation on-device without network connectivity required, and no need for users to take any action.
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MobileIron
Published By: Hortonworks     Published Date: Apr 05, 2016
Download this whitepaper to learn how Hortonworks Data Platform (HDP), built on Apache Hadoop, offers the ability to capture all structured and emerging types of data, keep it longer, and apply traditional and new analytic engines to drive business value, all in an economically feasible fashion. In particular, organizations are breathing new life into enterprise data warehouse (EDW)-centric data architectures by integrating HDP to take advantage of its capabilities and economics.
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Hortonworks
Published By: Oracle     Published Date: Feb 21, 2018
The new integration demands of cloud, mobile, API and IoT technologies require a unified approach to integration that spans multiple integration channels. As the volume, variety, and velocity of data increases it is becoming more important to maintain effective communication among all elements of the architecture. Planning your cloud, hybrid, and on-premises integration needs? This free e-book will show you how the Oracle Cloud Platform for Integration can help you extend what is possible.
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Oracle
Published By: Juniper Networks     Published Date: Aug 08, 2017
Business evolution and technology advancements during the last decade have driven a sea change in the way data centers are funded, organized, and managed. Enterprises are now focusing on a profound digital transformation which is a continuous adjustment of technology management resources to deliver business results, guided by rapid review of desired outcomes related to end clients, resources, and budget constraints. These IT transitions are very much part of the competitive landscape, and executed correctly, they become competitive differentiators and enable bottom line growth. These outcomes are driving data centers to virtualization, service-oriented architectures, increased cybersecurity, “big data,” and “cloud,” to name a few of the key factors. This is completely rethinking and retooling the way enterprises handle the applications, data, security, and access that constitute their critical IT resources. In essence, cloud is the new IT.
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Juniper Networks
Published By: Juniper Networks     Published Date: Aug 08, 2017
Juniper Networks hybrid cloud architecture enables enterprises to build secure, high performance environments across private and public cloud data centers. The easy-tomanage, scalable architecture keeps operational costs down, allowing users to do more with fewer resources. Security is optimized by the space-efficient Juniper Networks® SRX Series Services Gateways, which are next-generation firewalls (NGFWs) with fully integrated, cloud-informed threat intelligence that offers outstanding performance, scalability, and integrated security services. Designed for high-performance security environments and seamless integration of networking, along with advanced malware detection with Juniper Sky™ Advanced Threat Prevention (ATP), application visibility and control, and intrusion prevention on a single platform, the SRX Series firewalls are best suited for enterprise hybrid cloud deployments.
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Juniper Networks
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