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Published By: Datastax     Published Date: Sep 27, 2019
Smartphones, smart cities, smart homes, smart cars—IoT has triggered a data explosion, and not every enterprise is prepared to handle it. Beyond collecting and analyzing the increasing volume of data, organizations must figure out how to manage the velocity of that data, as well as how to integrate it with multiple data sources. And that’s just scratching the surface of the IoT challenge. To extract business value out of this inpouring of data, and to take full advantage of IoT boosted by new 5G technology, IT organizations must consider five key technologies. In this ebook, you’ll learn about these five technologies and their benefits. To continue to develop and scale your IoT-driven applications, your infrastructure needs to be able to handle sensor data at velocity, keep data close to the edge, maintain 100% uptime, and make it easy to extract business value. The insights you’ll discover in this ebook will not only help you prepare your organization for this reality; they’ll also
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Datastax
Published By: IBM     Published Date: Jan 02, 2014
This study highlights the phases of the big data journey, the objectives and challenges of midsize organizations taking the journey, and the current state of the technology that they are using to drive results. It also offers a pragmatic course of action for midsize companies to take as they dive into this new era of computing.
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ibm, analytics, global business service, big data, business value, it professionals, volume, velocity
    
IBM
Published By: AWS - ROI DNA     Published Date: Aug 09, 2018
In today's big data digital world, your organization produces large volumes of data with great velocity. Generating value from this data and guiding decision making require quick capture, analysis and action. Without strategies to turn data into insights, the data loses its value and insights become irrelevant. Real-time data inegration and analytics tools play a crucial role in harnessing your data so you can enable business and IT stakeholders to make evidence-based decisions
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AWS - ROI DNA
Published By: Tricentis     Published Date: Jan 08, 2018
Data. It seems to be everywhere today and yet we can never get enough of it. But as it turns out, a lack of data isn’t our problem -- our problem is the difficulty piecing together, understanding and finding the story in all the data that’s in front of us. In software testing in particular, the need for consolidated, meaningful test metrics has never been higher. As both the pace of development and the cost of delivering poor quality software increase, we need these metrics to help us test smarter, better and faster. Fortunately, business intelligence now exists to make this goal a reality. The analytics these tools provide can help drive efficient and effective testing by providing teams with insight on everything from testing quality and coverage to velocity and more. And this knowledge can position the QA team as trusted experts to advise the entire software development team on steps that can ensure a better quality end result.
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Tricentis
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: Hewlett Packard Enterprise     Published Date: Oct 24, 2017
Big Data is not just a big buzzword. Government agencies have been collecting large amounts of data for some time and analyzing the data collected to one degree or another. Big data is a term that describes high volume, variety and velocity of information that inundates an organization on a regular basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and better services.
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cloud optimization, cloud efficiency, cloud management, cloud assurance, cloud visibility, enterprise management, data management
    
Hewlett Packard Enterprise
Published By: IBM     Published Date: Aug 05, 2014
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats. Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
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ibm, data, big data, information, healthcare, governance, technology
    
IBM
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: Cisco     Published Date: Jan 05, 2015
The four pillars of computing — cloud, mobility, social, and analytics — are driving new levels of network innovation in datacenter networks. These forces are now buffeting the datacenter along with virtualization and the Internet of Things (IoT), resulting in sweeping changes in traffic patterns that expose the limitations of traditional networks and their operational models. To become a resource rather than a bottleneck to overall datacenter performance, the network must deliver not just exceptional performance and scalability but also unprecedented automation and orchestration that can yield agility, flexibility, and service velocity. This Technology Spotlight examines these key trends and discusses the role that Cisco's Application Centric Infrastructure (ACI) plays in addressing these ongoing challenges for enterprise IT and network managers.
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cloud computing, infrastructure, marketplace, application, hybrid, enterprise
    
Cisco
Published By: IBM     Published Date: Sep 11, 2013
Learn how a better grasp of "big data" from consumers will help retailers generate valuable insights for personalizing marketing and improving the effectiveness of campaigns among other things.
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big data, retail, insights, marketing, campaigns, velocity of data
    
IBM
Published By: ForgeRock     Published Date: Mar 10, 2015
In the modern age, customers are in control of when, where, and how they consume information. In this environment, customer-focused companies strive to make their content available to an ever-growing number of connected users and devices on a much larger scale than ever before, and to gather as much data and insight from these interactions as possible. To achieve this securely, companies need identity and access management (IAM) platforms that are adaptable, scalable, responsive, and high velocity— not typical characteristics of employee-facing IAM, but all too typical of other systems of engagement. This species of solution is worthy of a unique name: identity relationship management (IRM).
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iam, irm, forrester, forgerock, platforms, relationship management
    
ForgeRock
Published By: DigitalOcean, LLC     Published Date: Nov 04, 2019
Over the last several years, DigitalOcean has built out its feature set in a way that has taken it from a provider of cheap and simple virtual machines (VMs) to a legitimate public cloud alternative, most recently adding support for Redis and MySQL to its managed database services. As its services evolve, it continues to experience healthy growth and maintain popularity among the independent developer audience. Over the past two years, DigitalOcean has accelerated the velocity of its feature development, looking to better meet the needs of a business audience. By emphasizing a simplified feature set, pricing structure and user experience, even as it expands its competitive footprint in the shadow of public cloud vendors such as Amazon Web Service (AWS), Microsoft Azure and Google Cloud Platform (GCP), Digital Ocean continues to help define what it means to be an alternative public cloud in a market dominated and largely defined by hyperscalers.
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DigitalOcean, LLC
Published By: IBM     Published Date: Oct 13, 2016
In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before. Download this white paper to learn how.
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database, big data, analytics, infrastructure
    
IBM
Published By: Nimble Storage     Published Date: Feb 26, 2016
Download this eBook to learn the steps you can take now to prepare for the all flash data center. flash storage, SSD, all flash data centers, nimble storage, predictive flash platform, application perfomance, data velocity
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flash storage, ssd, all flash data centers, nimble storage, predictive flash platform, application perfomance, data velocity, data protection
    
Nimble Storage
Published By: IBM     Published Date: Jul 06, 2017
Effectively using and managing information has become critical to driving growth in areas such as pursuing new business opportunities, attracting and retaining customers, and streamlining operations. In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before. These needs create a daunting array of workload challenges and place tremendous demands on your underlying IT infrastructure and database systems. In many cases, these systems are no longer up to the task—so it’s time to make a decision. Do you use more staff to keep up with the fixes, patches, add-ons and continual tuning required to make your existing systems meet performance goals, or move to a new database solution so you can assign your staff to new, innovative projects that move your business forward?
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database, growth, big data, it infrastructure, information management
    
IBM
Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache
    
Altiscale
Published By: Forte Wares     Published Date: Jun 24, 2014
The key to making big data initiatives a success lies within making the produced data more digestible and usable in decision making, rather than making it just ‘more,’ resulting in the creation of an environment wherein information is used to generate real impact. Put another way, the survival of Big Data is more about making the right data (not just higher volume) available to the right people (not just higher variety) at the right time (not just higher velocity).
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Forte Wares
Published By: Trifacta     Published Date: Feb 12, 2019
In recent years, a new term in data has cropped up more frequently: DataOps. As an adaptation of the software development methodology DevOps, DataOps refers to the tools, methodology and organizational structures that businesses must adopt to improve the velocity, quality and reliability of analytics. Widely recognized as the biggest bottleneck in the analytics process, data preparation is a critical element of building a successful DataOps practice by providing speed, agility and trust in data. Join guest speaker, Forrester Senior Analyst Cinny Little, for this latest webinar focusing on how to successfully select and deploy a data preparation solution for DataOps. The presentation will include insights on data preparation found in the Forrester Wave™: Data Preparation Solutions, Q4 2018. In this recorded webinar you will learn: • Where does data preparation fit within DataOps • What are the key technical & business differentiators of data preparation solutions • How to align the righ
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Trifacta
Published By: Docsend     Published Date: Apr 09, 2018
It’s no secret that content - from case studies to pitch decks - fuels the modern sales cycle. However, a vast majority of teams don't track prospect engagement with sales content, and if they do, it's unclear which metrics actually indicate success. This report, From Strategy to Execution: 6 Sales Content Benchmarks Every Business Needs, reveals 6 key benchmarks for how, when, and where prospects engage with sales content. In the report, you’ll find data-backed, actionable insights to help you: • Identify which sales content drives the sales cycle forward • Enable sellers to share the right content in their outreach • Build a more efficient, higher velocity sales pipeline
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Docsend
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: Dell     Published Date: Sep 17, 2014
As the 3 V’s of big data: volume, velocity and variety continue to grow, so too does the opportunity for finance sector firms to capitalize on this data for strategic advantage. Read how.
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dell, finance data, data storage, financial records, secure finance, cloud, finance cloud
    
Dell
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: 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: Dec 27, 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: Oracle     Published Date: Sep 26, 2019
The digital world produces data at an unprecedented scale and velocity. According to Gartner, by 2020 there will be more than 26 billion connected devices, producing exponentially more data. How can businesses understand and leverage this data so they can successfully adapt to changing customer expectations and business dynamics?
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Oracle
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