data sets

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Published By: Amazon Web Services     Published Date: Jul 25, 2018
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organizations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt. However, given big datas power to transform business, its critical that organizations overcome these challenges and realize the value of big data. Download now to find out more.
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Amazon Web Services
Published By: CA Technologies EMEA     Published Date: Sep 07, 2018
There are five ways to provision test data. You can copy or take a snapshot of your production database or databases. You can provision data manually or via a spreadsheet. You can derive virtual copies of your production database(s). You can generate subsets of your production database(s). And you can generate synthetic data that is representative of your production data but is not actually real. Of course, the first four examples assume that the data you need for testing purposes is available to you from your production databases. If this is not the case, then only manual or synthetic data provision is a viable option. Download this whitepaper to find out more about how CA Technologies can help your business and its Test Data problems.
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CA Technologies EMEA
Published By: SAS     Published Date: May 24, 2018
Compliance doesnt have to be a scary word, even when facing the multifaceted challenges of the European Unions General Data Protection Regulation. In fact, following through with plans for sustainable GDPR compliance can have many long-term benefits for your organization. That's because relying on data-driven decisions across the board sets you on track to gain a competitive edge. In February 2018, SAS conducted a survey with 183 global, cross-industry businesspeople involved with GDPR at their organizations. Based on the survey results, this e-book delves into the biggest challenges and opportunities organizations have faced on the road to GDPR compliance. Read on for advice from industry experts about the best path to compliance. Learn what steps your peers across a variety of industries have already taken and discover how an integrated approach from SAS can continue to guide your journey toward GDPR compliance.
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SAS
Published By: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. Youll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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SAS
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Amazon Redshift Spectruma single service that can be used in conjunction with other Amazon services and products, as well as external toolsis revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemmawith Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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Amazon Web Services
Published By: Adlib Software     Published Date: Jan 13, 2014
Welcome to the future of 24/7, any-time, anywhere access to digital content - where dynamic publishing solutions are the mantra. Is your organization ready for this brave new world of digital content distribution? This whitepaper explores how to prime your organization to leverage rapid digital content consumption as a key to business intelligence.
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content marketing, content management, enterprise content management, publishing, data, content transformation, document transformation, document conversion, sharepoint, filenet, documentum, ecm, opentext, archive, assets, digital assets, networking, it management, knowledge management, enterprise applications
    
Adlib Software
Published By: SAP     Published Date: May 18, 2014
In-memory technologyin which entire datasets are pre-loaded into a computers random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiatedhas 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.
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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, analytical applications
    
SAP
Published By: Equinix     Published Date: May 28, 2015
This paper illustrates different perspectives on building a secure and flexible data center platform that sustains growth and innovation and delivers a serious competitive advantage.
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data center, strategic business advantage, hybrid, technology, it demands, data center platform, equinix, equinix solutions, data, data integration, virtualization, cloud computing, infrastructure, design and facilities
    
Equinix
Published By: Amazon Web Services     Published Date: Dec 15, 2017
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance. In this webinar, youll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences. Join this webinar to learn: How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development. How AWS supports BI solutions f
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Amazon Redshift Spectruma single service that can be used in conjunction with other Amazon services and products, as well as external toolsis revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemmawith Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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Amazon Web Services
Published By: Oracle CX     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle CX
Published By: Delphix     Published Date: May 03, 2016
Today's test data management (TDM) solutions force teams to work with compromised data sets, and push testing to too late in the software development lifecycle. The end result is rework, delayed releases, and costly bugs that cripple production systems. Furthermore, prevailing approaches to test data management - including subsetting, synthetic data, shared environments, and standalone masking--represent flawed solutions that fail across one or more key dimensions.
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Delphix
Published By: Dell EMC     Published Date: Nov 04, 2016
As organizations continue to virtualize their infrastructures to gain higher levels of operational efficiency, VM sprawl and resource utilization are two key factors that can quickly create havoc for IT admins. In resource-siloed infrastructures, where multiple administrators are in charge of different pieces of the infrastructure, complexity continues to grow as mission-critical data sets and organizations grow. This is especially true in dynamic, mission-critical environments, where one wrong move or lack thereof could significantly impact an application, the end-user experience, or worst case, company revenue. The impact of hyper-converged infrastructures on IT has been profound. In fact, 85% of respondents to a recent ESG survey already use or plan to use a hyper-converged solution in the coming months. Though that number appears high, it is not all that surprising. ESG also asked organizations to identify which factors drove them to deploy or considering deploying a hyper-conv
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ubuntu, performance, windows, hyper-convergence, infrastructure, information management, data warehousing
    
Dell EMC
Published By: Dell EMC     Published Date: Feb 23, 2017
As organizations continue to virtualize their infrastructures to gain higher levels of operational efficiency, VM sprawl and resource utilization are two key factors that can quickly create havoc for IT admins. In resource-siloed infrastructures, where multiple administrators are in charge of different pieces of the infrastructure, complexity continues to grow as mission-critical data sets and organizations grow. This is especially true in dynamic, mission-critical environments, where one wrong move or lack thereof could significantly impact an application, the end-user experience, or worst case, company revenue.
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Dell EMC
Published By: Dun & Bradstreet     Published Date: Mar 03, 2017
Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry The democratization of financial services data and technology, together with more intense competition, makes the needs of todays market participants vastly different from those of previous generations. Firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments. By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models. View our research report to learn how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.
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Dun & Bradstreet
Published By: Panasonic     Published Date: Aug 04, 2016
Whether deploying, securing or managing mobility solutions in a professional environment, enterprise leaders must go to great lengths to ensure their investments are effective and easily assimilated into their existing workflows. In order to avoid encountering challenges with rugged mobile devices and other specialist mobile solutions, it is crucial for businesses with field workforces to leverage services and partners that can help them instill strategic best practices across the entire spectrum of mobilization, including security and data protection, installation and integration, training, maintenance and warranties, and proper disposal of old assets.
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mobile devices, mobility, managing mobility, best practices, productivity, bluetooth, mobile computing, business analytics
    
Panasonic
Published By: CA Technologies     Published Date: Apr 06, 2017
Privileged Access Management is an imperative to addressing PCI compliance. Yet its importance extends beyond just meeting PCI compliance requirements as it allows an organization to improve its overall security posture against todays external and internal threats. CA Privileged Access Manager provides an effective way to implement privileged access management in support of PCI compliance and other security needs.
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data assets, risk management, it security, session management, authentication management, security intelligence, information management, infrastructure management, database platforms, account management
    
CA Technologies
Published By: Oracle     Published Date: Oct 07, 2015
This Research Brief combines research from a number of Aberdeen Sales Effectiveness research data sets, to create a holistic view of the most effectively deployed CRM systems.
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Oracle
Published By: IBM     Published Date: Sep 22, 2011
Companies need capabilities for identifying data assets and relationships, assessing data growth and implementing tiered storage strategies-capabilities that information governance can provide. It is important to classify enterprise data, understand data relationships and define service levels. Database archiving has proven effective in managing continued application data growth especially when it is combined with data discovery.
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ibm, application data, technology, enterprise, database archive, application integration, analytical applications, database development, data quality, database security
    
IBM
Published By: CA Technologies     Published Date: Aug 22, 2017
Across industry sectors, the boundaries of the traditional enterprise are blurring, as organizations open up their on-premise data and application functionality to partner organizations, the Web, mobile apps, smart devices and the cloud. APIs (application programming interfaces) form the foundation of this new open enterprise, allowing enterprises to reuse their existing information assets across organizational boundaries.
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CA Technologies
Published By: Carbonite     Published Date: Apr 09, 2018
IT admins tasked with restoring servers or lost data during a disruption are consumed with a single-minded purpose: successful recovery. But it shouldnt take an adverse event to underscore the importance of recovery as part of an overall backup strategy. This is especially true with large datasets. Before you consider how youre going to back up large datasets, first consider how you may need to recover the data. Variables abound. Is it critical or non-critical data? A simple file deletion or a system-wide outage? A physical server running onsite or a virtual one hosted offsite? These and a handful of other criteria will determine your backup and disaster recovery (BDR) deployment. What do we mean by large? A simple question with a not-so-simple answer. If your total data footprint is 5 TB or more, thats considered large. But what kind of data is it? How many actual files are there? How frequently do they change? How much can they be compressed? Its likely that two different 5 TB en
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Carbonite
Published By: Juniper APAC DWA Cloud Leads July 2017     Published Date: Aug 10, 2017
Resistance to change is futile. Financial services are becoming more embedded in the banking customers everyday life, driving unprecedented levels of change across the industry. The unfolding digital economy is ushering a new era of technology adoption in banking. From cloud to open banking APIs, these play a defining role in enabling banks to create new digital products and services, refresh the bank branch, find new customer segments, and monetize underutilized data and information assets.
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opening banking, api, data, currency, cloud transformation, frictionless banking, business model disruption
    
Juniper APAC DWA Cloud Leads July 2017
Published By: IBM     Published Date: Apr 12, 2016
The digital transformation that organizations are striving for today requires a mindset of "no limits" in terms of securely exposing critical business assets for internal and external consumption. In order to be an agile, digital business, enterprises need to seamlessly connect across hybrid environments, applications, data and processes, keeping a keen eye on an open cloud strategy. They also need a secure integration framework to provide controlled access, a single dashboard providing performance insights, and easy access to digital assets for your developers. This webcast will explore all of these areas in depth, to support your enterprise's quest to become a next-generation digital business.
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ibm, digital business, api economy, api management, apim, api, enterprise applications, infrastructure
    
IBM
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"Since a decade the market has seen an outburst of data. With Managing and Securing Data is Crucial. With 3.8 Billion mobile users and 24 Zetta bytes of data created, and 100% attacks that target username and password credentials, managing and securing data has become a crucial and the most critical aspect for almost every Business. Oracles SPARC M7 processor, with Oracles Software in Silicon technology, is the industrys most advanced multi thread, multi-core processor with unique capabilities for information security. It sets the foundation for mission-critical cloud based server infrastructure with unprecedented levels of security, efficiency, and performance.
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Oracle PaaS/IaaS/Hardware
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracles Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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Oracle PaaS/IaaS/Hardware
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