data architecture

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By: Nutanix, Inc.     Published Date: May 02, 2013
Enterprise data-centers are straining to keep pace with dynamic business demands, as well as to incorporate advanced technologies and architectures that aim to improve infrastructure performance, scale and economics. meeting these requirements, however, often requires a complete rethinking of how data centers are designed and managed. Fortunately, many enterprise IT architects are leading cloud providers have already demonstrated the viability and the benefits of a more modern, software-defined data center. This Nutanix white paper examines eight fundamental steps leading to a more efficient, manageable and scalable data center.
Tags : nutanix, data-centers, dynamic business demands, advanced technologies, it management, knowledge management, enterprise applications, data management
     Nutanix, Inc.
By: Teradata     Published Date: Jun 12, 2013
Until now, huge amounts of data have been ignored or underutilized simply because the tools didn't exist to make sense of it all. The good news is that increasingly sophisticated software is combining with growing computing horsepower to produce new analytics platforms that can handle all types of data from disparate systems Organizations can extract intelligence from their data to significantly outperform their rivals on growth, earnings, and performance.
Tags : unified data architecture, big data, analytics
     Teradata
By: Teradata     Published Date: Jun 12, 2013
Download this paper to learn how Unified Data Architecture™ can bridge the gap between the business language of SQL, the extreme processing power of MapReduce, and the big data residing in Hadoop to provide a unified, high-performance big data analytics system for the enterprise.
Tags : data, big data, unified data architecture
     Teradata
By: MarkLogic     Published Date: Nov 08, 2016
In this report, learn to: 1. Do more with your structured and unstructured data 2. Combine disparate data sources without the limits of relational systems 3. Build applications faster within an agile data architecture Download now!
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     MarkLogic
By: MarkLogic     Published Date: Nov 15, 2016
In this report, learn to: 1. Do more with your structured and unstructured data 2. Combine disparate data sources without the limits of relational systems 3. Build applications faster within an agile data architecture Download now!
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     MarkLogic
By: MarkLogic     Published Date: Jun 19, 2017
Integrate your data silos to finally navigate the data maze. This comprehensive white paper addresses a key element in any GRC program: regulatory compliance. Download this paper to learn about the costs, current practices, and associated risks. We’ll also recommend a technologically-enabled approach and highlight its main business benefits for financial services, insurance, and energy companies. Overview Executive Summary Compliance Requirements A Recommended Approach Business Benefits Regulatory Compliance Solution Architecture Related Materials
Tags : banking, financial services, data management, silos, data strategy, transparency, data governance, regulation, data architecture, compliance, reporting, energy, insurance
     MarkLogic
By: MarkLogic     Published Date: Jun 19, 2017
There is opportunity in the ongoing turbulence throughout the financial industry; however, regulatory and compliance challenges absorb resources and distract focus away from growth. Download white paper to learn about a smart approach to data and innovative tools that build resilience into a business looking to not just weather the storm, but seize the advantage. Overview: - Executive Summary - Challenges of Financial Markets Today - The Need for a 360-View of Data - How to Deliver: Increased Transparency Adapt to Evolving Regulations Improve Compliance and Lower Cost - Solution Architecture for Regulatory Reporting - Case Study: Adapting to Regulatory Complexity "Disparate systems led to frequent cycles spent correcting errors within the data”.
Tags : banking, financial services, data management, silos, data strategy, transparency, data governance, regulation, data architecture, compliance, reporting
     MarkLogic
By: MarkLogic     Published Date: Jun 21, 2017
Remember Y2K – the IT “problem” that should have brought businesses to a screeching half? Despite the hype, the preparation led to widely documented business benefits and uncovered new opportunities that transformed organizations worldwide. Similarly in today’s highly regulated Financial Services industry, enabling GRC by integrating data from silos can be the driver for future business use cases like machine learning and anti-fraud detection services. GRC can be your catalyst for new opportunities. Listen in as our panel of financial services experts discuss the keys to reinventing your data strategy. We’ll cover: How to align your GRC strategy with a business transformation agenda How to ensure your organization’s approach to data management isn’t just a one-off solution, but a comprehensive one adaptable to changing regulations How to navigate the increasing regulatory demands for granular data security How to turn your compliance spend into new opportunities to earn revenue
Tags : banking, financial services, data management, silos, data strategy, transparency, data governance, regulation, data architecture, compliance, reporting, business transformation, fraud, digital transformation
     MarkLogic
By: MarkLogic     Published Date: Jun 21, 2017
Global financial organizations are facing increasing demands from the business for more granularity, transparency, reporting and security. If you’re on the IT side, you know this adds a different set of ‘mores’ to the equation: More duplication, delays, and people. What’s the net-net? More cost and more risk. You can balance the scales to satisfy those demands. And it starts with thinking differently about data management. Our financial services technology experts will explore the implications of governance, risk and compliance (GRC) imperatives. You’ll learn: • Why data is at the heart of an effective and dynamic GRC strategy • Why technological capabilities used to enable standard GRC programs can reduce transparency and prevent you from gaining a holistic view of your data • A new approach to data can provide the business with complete transparency • Review a sample regulatory reporting architecture Stop burning time on tooling — and start building a dynamic GRC strategy that can
Tags : banking, financial services, data management, silos, data strategy, transparency, data governance, regulation, data architecture, compliance, reporting
     MarkLogic
By: MobileIron     Published Date: May 07, 2018
This 10-point checklist outlines best practices for designing a security architecture that protects cloud data at the endpoint. Enterprise computing architectures have changed fundamentally in the last ten years, as employees consume an ever-growing collection of business cloud services through mobile apps. The traditional security approach of network perimeter and locked-down endpoints is not suitable for this app-to-cloud model of modern work.
Tags : cloud, best, practices, endpoint, security, data
     MobileIron
By: MobileIron     Published Date: Aug 20, 2018
This 10-point checklist outlines best practices for designing a security architecture that protects cloud data at the endpoint. Enterprise computing architectures have changed fundamentally in the last ten years, as employees consume an ever-growing collection of business cloud services through mobile apps. The traditional security approach of network perimeter and locked-down endpoints is not suitable for this app-to-cloud model of modern work.
Tags : cloud, practices, audit, checklist, endpoint, security
     MobileIron
By: Looker     Published Date: Dec 03, 2015
The focus of modern business intelligence has been self-service; pushing data into the hands of end users more quickly with more accessible user interfaces so they can get answers fast and on their own. This has helped alleviate a major BI pain point: centralized, IT-dominated solutions have been too slow and too brittle to serve the business. What has been masked is a lack of innovation in data modeling. Data modeling is a huge, valuable component of BI that has been largely neglected. In this webinar, we discuss Looker’s novel approach to data modeling and how it powers a data exploration environment with unprecedented depth and agility. Topics covered include: • A new architecture beyond direct connect • Language-based, git-integrated data modeling • Abstractions that make SQL more powerful and more efficient
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     Looker
By: IBM     Published Date: Jul 05, 2016
The use of analytics will define the successful midmarket business of the next decade, just as it will define the successful larger organization in this information age. The past decade has seen rapid change in the business climates with an explosion of global competition and economic uncertainty. Despite this, many midmarket companies have thrived by using their size and agility as a competitive advantage. Certainly they have fewer resources than large corporations who are able to invest large sums in IT. However, small yachts can turn and adjust their course much quicker than large cruise ships. The same came be said for midmarket companies. The most successful organizations are ones that can react the quickest to changes in the market place. Midmarket companies can take advantage of their smaller size by being more agile and quicker to change, or even reinvent themselve
Tags : ibm, ibm pure data system, big data, data analytics, analytics architecture, competitive advantage, midmarket organization, knowledge management, data management
     IBM
By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
Tags : data warehouse, analytics, ibm, deployment models
     IBM
By: HotSchedules     Published Date: Apr 25, 2018
Restaurant technologies have transformed drastically over the past decade, however there have been several misconceptions about the impact big data can have on restaurant operations. According to a Hospitality Technology study, only 23% of restaurants have confidence in overall tech innovation. When it comes to the state of technology many restaurants are still stuck with disconnected architecture, complex legacy systems, lack of visibility, and high maintenance costs. These challenges ultimately intimidate restaurant operators to outdated systems and processes that significantly limit their business growth. This ebook looks at the four pressing challenges and presents one unifying solution.
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     HotSchedules
By: Datastax     Published Date: Aug 23, 2017
Enterprises today continue to differentiate themselves with cloud applications – any application that needs to be always-on, distributed, scalable, real-time, and contextual. With DataStax Enterprise, DataStax delivers comprehensive data management with a unique always-on architecture that accelerates the ability of enterprises, government agencies, and systems integrators to power the exploding number of cloud applications. DataStax Enterprise (DSE) powers these cloud applications that require data distribution across data centers and clouds, by using a secure, operationally simple platform. At its core, DSE offers the industry’s best distribution of Apache Cassandra™. This paper provides a summary of the features and functionality of DataStax Enterprise that make it the best choice for companies that are looking to leverage the promise of Apache Cassandra™ for production environments.
Tags : cloud, adoption, application, migration, performance
     Datastax
By: Druva     Published Date: Nov 09, 2018
The all-encompassing data center is a thing of the past. Modern data environments are distributed and include remote and branch offices, mobile devices, and the Internet of Things (IoT) as well as cloud solutions such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). There’s more critical data to back up than ever before. Plus, data silos and fragmented management mean poor visibility, which can make it difficult to comply with regional data residency and security rules as well as service-level agreements (SLAs). On-premises data protection has not kept pace. According to a survey by Fujitsu, 45% of IT managers said they had lost data or productivity related to data protection inefficiency within the last year.1 Often, this is because on-premises backups are cumbersome and do not always happen on time.
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     Druva
By: Oracle + Dyn     Published Date: Jun 29, 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
By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
Tags : data warehouse, analytics, hybrid data warehouse, development model
     Group M_IBM Q1'18
By: Cohesity     Published Date: Nov 20, 2018
Today, hybrid cloud is increasingly the norm, and enterprises are challenged with ways to have visibility, manage and make use of all this data – both on-premises and in the cloud. While much attention has been given to primary data affecting mission-critical workloads, data for secondary workloads – backup, test/dev, disaster recovery, and archiving to name a few – have become siloed the same way application data has, leading to multiple point solutions to manage an increasing amount of data. This white paper looks at the evolution of these challenges and offers practical advice on ways to store, manage and move secondary data in hybrid cloud architectures while extracting the hidden value it can provide. Download to learn more.
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     Cohesity
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.
Tags : systemware, content management, digital transformation, content cloud, top key trends, intelligent information, data storage, enterprise content management, it management, content services, enterprise architecture
     Systemware
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.
Tags : systemware, content management, digital transformation, content cloud, top key trends, intelligent information, data storage, enterprise content management, it management, content services, enterprise architecture
     Systemware
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
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.
Tags : 
     Group M_IBM Q2'19
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