data volume

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Published By: HP     Published Date: Feb 02, 2015
Ever-increasing data volumes driven by the constant growth in both structured and unstructured data coupled with the ever decreasing costs of storage capacity on a per GB basis are continuing to put a strain on corporate backup abilities. While other backup and data optimization technologies offer some relief, deduplicating backup appliances have become the go to solution. They provide a quick, largely non-disruptive plug-and-play solution that alleviates backup pain, reduces storage consumption by up to 20x and have become a proven frontrunner in the ongoing battle to improve the backup experience.
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HP
Published By: Delphix     Published Date: May 28, 2015
"Security-conscious organizations face a gap between current requirements and capabilities as they relate to data masking. Data volumes are growing exponentially and the risk of data leaks continues to make news, yet many organizations rely on inefficient, legacy approaches to protecting sensitive data. In contrast, top performing companies are turning to virtual databases and service-based masking solutions to ensure that data management functions can keep up with software development.
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Delphix
Published By: HP     Published Date: Feb 11, 2015
Ever-increasing data volumes driven by the constant growth in both structured and unstructured data coupled with the ever decreasing costs of storage capacity on a per GB basis are continuing to put a strain on corporate backup abilities. While other backup and data optimization technologies offer some relief, deduplicating backup appliances have become the go to solution. They provide a quick, largely non-disruptive plug-and-play solution that alleviates backup pain, reduces storage consumption by up to 20x and have become a proven frontrunner in the ongoing battle to improve the backup experience.
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HP
Published By: MarkLogic     Published Date: Mar 13, 2015
Big Data has been in the spotlight recently, as businesses seek to leverage their untapped information resources and win big on the promise of big data. However, the problem with big data initiatives are that organizations try using existing information management practices and legacy relational database technologies, which often collapse under the sheer weight of the data. In this paper, MarkLogic explains how a new approach is needed to handle the volume, velocity, and variety of big data because the current relational model that has been the status quo is not working. Learn about the NoSQL paradigm shift, and why NoSQL is gaining significant market traction because it solves the fundamental challenges of big data, achieving better performance, scalability, and flexibility. Learn how MarkLogic’s customers are reimagining their data to: - Make the world more secure - Provide access to valuable information - Create new revenue streams - Gain insights to increase market share - Reduce b
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enterprise, nosql, relational, databases, data storage, management system, application, scalable
    
MarkLogic
Published By: K2     Published Date: Apr 27, 2015
This paper describes how a business-app platform from K2® can help you provide simpler, more cost-effective ways to empower people by connecting them with data that is dispersed across different systems.
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erp and legacy systems, data volume, business processes, cloud crm system, system complexity, business apps
    
K2
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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Waterline Data & Research Partners
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: 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: 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: Jun 04, 2019
This survey report shows that while finance departments take responsibility for ensuring there are data-management strategies in place that include securing their organisation’s data, they struggle to manage the huge volumes of data and gain valuable insights from it. This affects both their confidence in the security of the data and their ability to manage it ethically. The number one priority of finance leaders is to enforce technologies that enable insights instantly, any place, anytime, to enable better forecasting. Read report
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Oracle
Published By: Oracle     Published Date: Jun 04, 2019
Data is central to marketing function, yet research shows that 50% of CMOs struggle to manage data volumes, or gain insights. Read how to address data ethics and management in your organisation. Marketing’s top priority is to enforce technologies enabling real time data insights to drive instant, personalised CX. Meanwhile, only 30% of CMOs say they have data visualisation dashboards to analyse specific sets of data. Read the findings
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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, data volume, data velocity, data variability, data management, data collection, data processing, data scaling, data storage systems, data access, data solutions, data challenges, management tooling
    
Red Hat, Inc.
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