data volume

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Published By: Mimecast     Published Date: Oct 11, 2018
Information management is getting harder. Organizations face increasing data volumes, more stringent legal and regulatory record-keeping requirements, stricter privacy rules, increasing threat of breaches and decreasing employee productivity. Companies are also finding that their old-fashioned, legacy archive strategies are increasingly ineffective. This is driving many organizations to rethink their approach, developing more modern Information Governance strategies.
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Mimecast
Published By: Mimecast     Published Date: Jul 15, 2019
The Mimecast Supervision solution enables compliance personnel to systematically review and discover targeted data among the volume of communications organizations face today. Integrated with the industry leading Mimecast Cloud Archive, users can facilitate an auditable, managed supervision review process, flexible to meet the needs of the business while utilizing a scaleable, immutable SEC 17A-4 validated and tamper proof archive with guaranteed 7 second SLA search capabilities. To reduce the number of false positives in sampling data, targeted detection rules can focus on specific senders/recipients and to accelerate the process. In addition, queues can be configured with an upper limit upon which to be populated with email. This helps limit the amount of email a reviewer must go through while still identifying risk. Today’s supervision demands require reviewers to be highly productive.
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Mimecast
Published By: Akamai Technologies     Published Date: Apr 25, 2018
Cyber attackers are targeting the application programming interfaces (APIs) used by businesses to share data with customers. Consumer mobile adoption, electronic goods and services, and high volumes of data have led businesses to use APIs for data exchange. Unfortunately, attackers can also use APIs to access or deny service to valuable data and systems. This white paper explores strategies for protecting APIs. You’ll learn about APIs, how and why these endpoints are targets for web application attacks, security models, and how Akamai can help.
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api, security, interface, businesses, data, mobile, adoption
    
Akamai Technologies
Published By: IBM     Published Date: Oct 21, 2016
The greatest challenge of the big data revolution is making sense of all the information generated by today's vast digital economy. It's well enough for an organization to collect every slice of data it can reach, but how does it extract value from this massive volume of information?
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ibm, analytics, data science, data, big data, aps data, aps
    
IBM
Published By: IBM     Published Date: May 23, 2017
Flexible deployment options, licensing models help take the challenges out of change. As you move toward the cloud, you're likely planning or managing a mixed environment of on- premises and on- cloud applications. To help you succeed in this transition, you need a trans-formative, mixed-workload database that can handle a massive volume of data while delivering high performance, data availability and the flexibility to adapt respond to business changes.
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cloud applications, mobile optimization, web-based applications, data availability, ibm, db2
    
IBM
Published By: IBM     Published Date: Oct 17, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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IBM
Published By: IBM     Published Date: Nov 08, 2017
Flexible deployment options, licensing models help take the challenges out of change. As you move toward the cloud, you're likely planning or managing a mixed environment of on- premises and on- cloud applications. To help you succeed in this transition, you need a trans-formative, mixed-workload database that can handle a massive volume of data while delivering high performance, data availability and the flexibility to adapt respond to business changes.
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ibm, cloud, cloud computing, database, ibm db2
    
IBM
Published By: IBM     Published Date: Nov 08, 2017
Flexible deployment options, licensing models help take the challenges out of change. As you move toward the cloud, you're likely planning or managing a mixed environment of on- premises and on- cloud applications. To help you succeed in this transition, you need a trans-formative, mixed-workload database that can handle a massive volume of data while delivering high performance, data availability and the flexibility to adapt respond to business changes.
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ibm db2, cloud, on-cloud applications, mixed-workload database
    
IBM
Published By: DataStax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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DataStax
Published By: Datastax     Published Date: Aug 23, 2017
About 10 years ago big data was quickly becoming the next big thing. It surged in popularity, swooning into the tech world's collective consciousness and spawning endless start-ups, thought pieces, and investment funding, and big data's rise in the startup world does not seem to be slowing down. But something's been happening lately: big data projects have been failing, or have been sitting on a shelf somewhere and not delivering on their promises. Why? To answer this question, we need to look at big data's defining characteristic - or make that characteristics, plural - or what is commonly known as 'the 3Vs": volume, variety and velocity.
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datastax, big data, funding
    
Datastax
Published By: Datastax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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Datastax
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
Flexible deployment options, licensing models help take the challenges out of change. As you move toward the cloud, you're likely planning or managing a mixed environment of on- premises and on- cloud applications. To help you succeed in this transition, you need a trans-formative, mixed-workload database that can handle a massive volume of data while delivering high performance, data availability and the flexibility to adapt respond to business changes.
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cloud applications, database, data volume, data availability
    
Group M_IBM Q1'18
Published By: IBM     Published Date: Jul 05, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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IBM
Published By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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Group M_IBM Q418
Published By: Group M_IBM Q3'19     Published Date: Jun 27, 2019
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data.
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Group M_IBM Q3'19
Published By: Group M_IBM Q3'19     Published Date: Sep 04, 2019
In the last few years we have seen a rapid evolution of data. The need to embrace the growing volume, velocity and variety of data from new technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) has been accelerated. The ability to explore, store, and manage your data and therefore drive new levels of analytics and decision-making can make the difference between being an industry leader and being left behind by the competition. The solution you choose must be able to: • Harness exponential data growth as well as semistructured and unstructured data • Aggregate disparate data across your organization, whether on-premises or in the cloud • Support the analytics needs of your data scientists, line of business owners and developers • Minimize difficulties in developing and deploying even the most advanced analytics workloads • Provide the flexibility and elasticity of a cloud option but be housed in your data center for optimal security and compliance
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Group M_IBM Q3'19
Published By: IBM     Published Date: Jun 25, 2018
Vast resources of data are increasingly available, but the sheer volume can overwhelm human capability. By implementing the cognitive system of IBM Watson Discovery into their infrastructure, businesses can extract deeper and more accurate insights by efficiently identifying, collecting and curating structured and unstructured data. Watson Discovery, also capable of creating content collections and custom cognitive applications, can transform organizational processes to extend proprietary content and expert knowledge faster and at greater scales. Read more to learn how Watson Discovery can keep your organization evolving ahead of the competition. Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
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IBM
Published By: MarkLogic     Published Date: Nov 07, 2017
Today, data is big, fast, varied and constantly changing. As a result, organizations are managing hundreds of systems and petabytes of data. However, many organizations are unable to get the most value from their data because they’re using RDBMS to solve problems they weren’t designed to fix. Why change? In this white paper, we dive into the details of why relational databases are ill-suited to handle the massive volumes of disparate, varied, and changing data that organizations have in their data centers. It is for this reason that leading organizations are going beyond relational to embrace new kinds of databases. And when they do, the results can be dramatic.
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MarkLogic
Published By: Oracle     Published Date: Sep 30, 2019
Company reputations are at stake— unless they’re sure of their data security and practices in the face of multiplying data volumes. There is now a need to understand the responsibilities and the need for the entire company to commit to a digital trust framework. In this episode of the Tomorrow Talks series, IDC and Oracle discuss how data security and practices are no longer the remit of CIOs and CISOs alone. To find out more, watch this webinar today.
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Oracle
Published By: Oracle     Published Date: Sep 09, 2019
Company reputations are at stake— unless they’re sure of their data security and practices in the face of multiplying data volumes. There is now a need to understand the responsibilities and the need for the entire company to commit to a digital trust framework. In this episode of the Tomorrow Talks series, IDC and Oracle discuss how data security and practices are no longer the remit of CIOs and CISOs alone. To find out more, watch this webinar today.
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Oracle
Published By: IBM APAC     Published Date: Mar 19, 2018
Unstructured data has exploded in volume over the past decade. Unstructured data, media files and other data can be created just about anywhere on the planet using almost any smart device available today. As the amount of unstructured data grows exponentially, customers using this data need to be able to take advantage of the right storage solutions to support all of their file and object data requirements. IBM® recently added a new storage system to their Spectrum product family, IBM Spectrum Network Attached Storage (NAS). IBM Spectrum NAS adds another software-defined file storage system to IBM’s current unstructured data storage solutions, IBM Spectrum Scale™ and IBM Cloud Object Storage (COS). Below, we will discuss the three systems and supply some guidance on when and where to use each of them.
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IBM APAC
Published By: Red Hat, Inc.     Published Date: Jul 10, 2012
Today, as IT departments struggle to design and implement solutions capable of managing exponential data growth with strict requirements for application scale and performance, many of them are turning to in-memory data grids (IMDGs).
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it departments, data growth, managing data growth, application scale, application performance, in-memory data grids, imdgs, big data
    
Red Hat, Inc.
Published By: McAfee     Published Date: Nov 07, 2014
Segundo o relatório “Agulha em um palheiro de dados” (Needle in a Datastack), as empresas estão vulneráveis a violações de segurança porque não são capazes de analisar ou armazenar adequadamente o Big Data. Esses volumes cada vez maiores de eventos, bem como de dados sobre ativos, ameaças, usuários e outros dados relevantes, criaram um grande desafio para as equipes de segurança em relação ao Big Data. Para solucionar esse desafio, as empresas abandonaram as arquiteturas tradicionais de gerenciamento de dados para adotar sistemas dedicados ao gerenciamento de dados de segurança na era das APTs (ameaças persistentes avançadas).
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siem, big security data, segurança do big data, informações de segurança, ameaças avançadas, ameaças persistentes avançadas, apt, inteligência de segurança
    
McAfee
Published By: MarkLogic     Published Date: Mar 29, 2018
Real World Evidence (RWE) requires the correlation of complex, frequently changing, unstructured data. To the enterprise architect, that means extracting value from data that doesn't neatly fit solutions. In this white paper, we dive into the details of why relational databases are ill-suited to handle the massive volumes of disparate, varied, and changing data that is required to be successful with RWE. It is for this reason that leading life science organizations are going beyond relational to embrace new kinds of databases. And when they do, the results can be dramatic.
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data, integration, volume, optimization, architect, enterprise
    
MarkLogic
Published By: AWS     Published Date: Mar 22, 2018
Healthcare and Life Sciences organizations are adopting cloud-based workloads at a significant pace. A 2017 HIMSS study found that 65% of Healthcare organizations were using cloud-based services, and nearly 88% of those organizations were utilizing Software-as-a-Service (SaaS) solutions, which have become the preferred deployment method for many clinical application vendors. This eBook highlights advantages of using AWS to create and maintain cloudbased Next-Gen BI Solutions for Healthcare and Life Sciences organizations. This includes use cases from diverse organizations that have utilized AWS and APN Partners to manage and analyze data, and to discover insights otherwise obscured by the sheer volume of available information. Solutions from APN Partners can help your organization take the next step in building robust processes for making data-driven decisions that improve patient care, organizational processes, and innovative product development efforts.
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AWS
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