data types

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Published By: Arcserve     Published Date: May 29, 2015
Find out what you need to know about data protection appliances in this video brief by the Enterprise Strategy Group’s Sr. Data Protection Analyst, Jason Buffington. In this short video he’ll analyze: -Various types of data protection appliances -Market trends regarding appliance adoption -What data protection specialists expect from appliances -How Arcserve has evolved to solve the issues of today’s complex organizations Watch this video brief today!
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data protection appliances, appliance adoption, arcserve, security
    
Arcserve
Published By: SAS     Published Date: Apr 13, 2011
This paper will outline the value and methods involved in data mining across both quantitative and qualitative data. In addition, it will describe the data transformations necessary before doing such work, and the tools that are particularly valuable for mining mixed data types.
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business intelligence, michael lock, predictive analytics, business insight, business visibility, small to medium sized business
    
SAS
Published By: Splunk     Published Date: Apr 16, 2012
Discover a unique approach to handling large, semi-structured or unstructured time-series data. Splunk can be deployed in a matter of days to provide rapid cross-correlation between different data types-giving you unprecedented operational visibility.
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splunk, data, analyzying, decision making, ime-series data, log management, log management software, manage logs, analyze logs, log analyzer, security log analysis, log management intelligence, log management compliance, compliance, log management operations, operations, operational intelligence
    
Splunk
Published By: ForgeRock     Published Date: May 05, 2015
The goal of ESG Lab reports is to educate IT professionals about data center technology products for companies of all types and sizes. ESG Lab reports are not meant to replace the evaluation process that should be conducted before making purchasing decisions, but rather to provide insight into these emerging technologies. Our objective is to go over some of the more valuable feature/functions of products, show how they can be used to solve real customer problems and identify any areas needing improvement. ESG Lab's expert third-party perspective is based on our own hands-on testing as well as on interviews with customers who use these products in production environments. This ESG Lab report was sponsored by ForgeRock.
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esg, forgerock, identity platform, self-service, third-party authentication
    
ForgeRock
Published By: IBM     Published Date: May 02, 2014
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
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ibm, big data platform, architecting big data, analytics, intelligent business strategies, data complexity, data types, workload growth, workload complexity, big data analytic applications, operational decisions, multi-structured data, querying data, scalable data management, analytical ecosystem, hadoop solutions
    
IBM
Published By: IBM     Published Date: Apr 09, 2015
The information explosion, the proliferation of endpoint devices, growing user volumes and new computing models like cloud, social business and big data have created new vulnerabilities. Data security is a moving target—as data grows, more sophisticated threats emerge; the number of regulations increase; and changing economic times make it difficult to secure and protect data. Because data is a critical component of daily business operations, it is essential to ensure privacy and protect data no matter where it resides. Different types of information have different protection requirements; therefore, organizations must take a holistic and systematic approach to safeguarding information.
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ibm, cloud, social business, big data, data security, data protection, privacy
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
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ibm, data, magic quadrant, data management, analytics
    
IBM
Published By: IBM     Published Date: Apr 06, 2016
The information explosion, the proliferation of endpoint devices, growing user volumes and new computing models like cloud, social business and big data have created new vulnerabilities. Data security is a moving target—as data grows, more sophisticated threats emerge; the number of regulations increase; and changing economic times make it difficult to secure and protect data. Because data is a critical component of daily business operations, it is essential to ensure privacy and protect data no matter where it resides. Different types of information have different protection requirements; therefore, organizations must take a holistic and systematic approach to safeguarding information.
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ibm, data security, risk management, big data
    
IBM
Published By: Group M_IBM Q418     Published Date: Sep 25, 2018
There’s no getting around it. Passed in May 2016, the European Union (EU) General Data Protection Regulation (GDPR) replaces the minimum standards of the Data Protection Directive, a 21-year-old system that allowed the 28 EU member states to set their own data privacy and security rules relating to the information of EU subjects. Under the earlier directive, the force and power of the laws varied across the continent. Not so after GDPR went into effect May 25, 2018. Under GDPR, organizations are subject to new, uniform data protection requirements—or could potentially face hefty fines. So what factors played into GDPR’s passage? • Changes in users and data. The number, types and actions of users are constantly increasing. The same is true with data. The types and amount of information organizations collect and store is skyrocketing. Critical information should be protected, but often it’s unknown where the data resides, who can access it, when they can access it or what happens once
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Group M_IBM Q418
Published By: IBM     Published Date: Oct 13, 2017
Integrated Threat Management For Dummies, IBM Security Limited Edition, lays the foundation for effective tools and techniques that work together to help counter today’s advanced threats. Regardless of your role in the IT security organization, threat management tools and techniques will influence your job. Your role determines the part you play to effectively manage threats, including those targeting the cloud and your company’s data. If you are a Chief Information Security Officer (CISO) or security manager, this book explains in detail the types of tools you need to effectively prevent, detect, and respond to security incidents. If you’re in general business management, you’ll better understand the risks associated with enterprise computing and the reasons why a comprehensive portfolio of security tools that work well together is so important.
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malware prevention, network intrusion prevention, malware protection, ibm, firewall, mutating malware, cyber threats
    
IBM
Published By: SWsoft     Published Date: Aug 08, 2007
As virtualization becomes more pervasive in the datacenter, organizations are deploying complementary types of virtualization technologies. Read this white paper to learn how blending application and OS virtualization using Citrix and Virtuozzo will provide stronger and more robust virtualization solution.
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os virtualization, virtualization, citrix, best practices, swsoft, sw soft, application virtualization, server virtualization, network management, paravirtualization
    
SWsoft
Published By: SAS     Published Date: Aug 28, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: IBM     Published Date: Jan 09, 2014
According to Dr. Barry Devlin of 9sight Consulting, the truth behind all the talk about big data and the possibilities it can offer is not hard to see, provided that organizations are willing to return to the principles of good data management processes.
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ibm, big data, 9sight consulting, data, it management, maximize business, deployment, business opportunities, big data usage, data warehouse, data center, business analytics, big data offerings, core business data, analytic data, puredata system, data virtualization, data integration, data types, data quality
    
IBM
Published By: Cisco     Published Date: Dec 21, 2016
The data center infrastructure is central to the overall IT architecture. It is where most business-critical applications are hosted and various types of services are provided to the business. Proper planning of the data center infrastructure design is critical, and performance, resiliency, and scalability need to be carefully considered. Another important aspect of the data center design is the flexibility to quickly deploy and support new services. Designing a flexible architecture that can support new applications in a short time frame can result in a significant competitive advantage. The basic data center network design is based on a proven layered approach that has been tested and improved over the past several years in some of the largest data center implementations in the world. The layered approach is the foundation of a data center design that seeks to improve scalability, performance, flexibility, resiliency, and maintenance.
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Cisco
Published By: Altiscale     Published Date: May 28, 2015
Big Data technologies are maturing and quickly moving into the next phase - one that expands in data use-cases as Hadoop moves into more influential roles throughout IT infrastructures. In this just-released report, Gartner is recognizing four Big Data vendors as "cool." Gartner says these vendors can meaningfully and synergistically combine multiple types of functionality.
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big data, vendors, functionality, cool vendors, consistent performance, it management, data management; add - gartner, hadoop
    
Altiscale
Published By: BrightEdge     Published Date: Nov 13, 2014
BrightEdge tapped into its massive Data Cube repository to provide a comprehensive view into the channels that drive traffic and the types of content that perform best. BrightEdge created this report to help brands understand the actual performance of site content by channel and by industry.
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brightedge, content marketing, data analytics, marketing data, marketing platform, market intelligence, business intelligence
    
BrightEdge
Published By: VMware     Published Date: Dec 15, 2016
Organizations that invest in proprietary applications and data for competitive advantage in their industries only succeed by making those investments available to employees, customers, and partners that drive revenue and opportunity. Securing those investments requires a fresh perspective as the types of devices accessing the cloud datacenter are changing rapidly and new workloads, such as VDI desktops, are appearing more regularly alongside server workloads. These changes alter the potential attacks and potential threats to datacenters whose security primarily stands firm at the perimeter, however, within the data center the security is weak. By combining VMware NSX with the AirWatch Tunnel and/or VMware Horizon View, organizations are able to bridge the device to datacenter security gap in a way that both increases the overall security of the cloud datacenter and makes it far simpler to manage security through defining and delegating application and services to specific users. Thi
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VMware
Published By: SAS     Published Date: Oct 18, 2017
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: Citrix     Published Date: Jul 25, 2014
Managing mobile devices, data and all types of apps—Windows, datacenter, web and native mobile—through a single solution.
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enterprise, mobility, challenge, data, windows, datacenter, web, solution
    
Citrix
Published By: Symantec     Published Date: Jul 11, 2014
Today's datacenters face a gauntlet of challenges including protection of physical and virtual environments, fast recovery of data, reducing backup times and storage requirements, server consolidation, and disaster recovery. How are savvy CIO's conquering these types of challenges? Find out how in this white paper by Expert, David Davis.
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data, protection, physical and virtual environment, recovery, storage, consolidation, cio's
    
Symantec
Published By: AlienVault     Published Date: Oct 21, 2014
While vulnerability assessments are an essential part of understanding your risk profile, it's simply not realistic to expect to eliminate all vulnerabilities from your environment. So, when your scan produces a long list of vulnerabilities, how do you prioritize which ones to remediate first? By data criticality? CVSS score? Asset value? Patch availability? Without understanding the context of the vulnerable systems on your network, you may waste time checking things off the list without really improving security. Join AlienVault for this session to learn: • The pros & cons of different types of vulnerability scans - passive, active, authenticated, unauthenticated • Vulnerability scores and how to interpret them • Best practices for prioritizing vulnerability remediation • How threat intelligence can help you pinpoint the vulnerabilities that matter most
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vulnerability, management, risk, prioritize, profile, environment, data, asset value, network, authenticated, unauthenticated, remediation, best practices, intelligence, scores, attacks, policy violations, compromise, ex filtration, exploit
    
AlienVault
Published By: IBM     Published Date: Dec 06, 2013
Data workloads are rapidly evolving and changing. Today's enterprises have many different types of applications, with different usage patterns, all constantly accessing data. As a result, data services need to be more robust and scalable. IBM PureApplication System and IBM PureData™ System for Transactions are designed to meet these needs. This paper shows how the latest technology and expertise built into these systems gives businesses an innovative approach to rapidly create and manage highly scalable data services, without the complexity of traditional approaches.
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ibm, ibm puredata system, ibm pureapplication system, data, data center, data management, applications, workloads, data services, scalable, scalability, availability
    
IBM
Published By: IBM     Published Date: Nov 19, 2014
As the use of mobile devices exponentially expands, so too does security threats to the increasing number of mobile applications that companies rely on. As a result, companies struggle to keep pace with mobile application security and face the risk of embarrassing and costly data breaches. In this technical session, you’ll learn how Worklight Application Scanning helps you deliver applications that aren’t susceptible to the most common types of malware, including SQL Injection and Cross-Site Scripting. In addition, you’ll learn how this powerful tool helps address the OWASP Top 10 Mobile Risks for 2014.
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application screening, user feedback, mobile app development, cloud-based services
    
IBM
Published By: AlienVault     Published Date: Mar 30, 2016
Given that Point of Sale (POS) systems are used to transmit debit and credit card information in retail transactions, it's no wonder they are a desirable target for attackers. In this white paper, you'll learn about some of the common types of POS malware, how they work and best practices for protecting cardholder data. Topics covered in this white paper include: • Common types of POS malware and how they work • How attackers exfiltrate data from POS systems once they gain access • POS security techniques to protect payment card data Download your copy today to learn how to effectively detect and respond to POS malware threats.
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AlienVault
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