big data analytics

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By: IBM     Published Date: Sep 02, 2014
Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.
Tags : ibm, ibm platform computing, save money
     IBM
By: IBM     Published Date: May 20, 2015
Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.
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     IBM
By: IBM     Published Date: May 20, 2015
IBM Platform Symphony - accelerate big data analytics – This demonstration will highlight the benefits and features of IBM Platform Symphony to accelerate big data analytics by maximizing distributed system performance, fully utilizing computing resources and effectively harnessing the power of Hadoop.
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     IBM
By: IBM     Published Date: May 20, 2015
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: IBM     Published Date: May 20, 2015
According to our global study of more than 800 cloud decision makers and users are becoming increasingly focused on the business value cloud provides. Cloud is integral to mobile, social and analytics initiatives – and the big data management challenge that often comes with them and it helps power the entire suite of game-changing technologies. Enterprises can aim higher when these deployments are riding on the cloud. Mobile, analytics and social implementations can be bigger, bolder and drive greater impact when backed by scalable infrastructure. In addition to scale, cloud can provide integration, gluing the individual technologies into more cohesive solutions. Learn how companies are gaining a competitive advanatge with cloud computing.
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     IBM
By: IBM     Published Date: May 20, 2015
There is a lot of hype around the potential of big data and organizations are hoping to achieve new innovations in products and services with big data and analytics driving more concrete insights about their customers and their own business operations. To meet these challenges, IBM has introduced IBM® Spectrum Scale™. The new IBM Spectrum Scale storage platform has grown from GPFS, which entered the market in 1998. Clearly, IBM has put significant development into developing this mature platform. Spectrum Scale addresses the key requirements of big data storage - extreme scalability for growth, reduced overhead of data movement, easy accessibility , geographic location independence and advanced storage functionality. Read the paper to learn more!
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     IBM
By: IBM     Published Date: Sep 16, 2015
Building applications for handling big data requires laser-like focus on solutions that allow you to deliver scalable, reliable and flexible infrastructure for fast-growing analytics environments. This paper provides 6 best practices for selecting the “right” infrastructure—one that is optimized for performance, flexibility and long-term value.
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     IBM
By: Storiant     Published Date: Mar 16, 2015
Read this new IDC Report about how today's enterprise datacenters are dealing with new challenges that are far more demanding than ever before. Foremost is the exponential growth of data, most of it unstructured data. Big data and analytics implementations are also quickly becoming a strategic priority in many enterprises, demanding online access to more data, which is retained for longer periods of time. Legacy storage solutions with fixed design characteristics and a cost structure that doesn't scale are proving to be ill-suited for these new needs. This Technology Spotlight examines the issues that are driving organizations to replace older archive and backup-and-restore systems with business continuity and always-available solutions that can scale to handle extreme data growth while leveraging a cloudbased pricing model. The report also looks at the role of Storiant and its long-term storage services solution in the strategically important long-term storage market.
Tags : storiant, big data, analytics implementations, cloudbased pricing model, long-term storage services solution, long-term storage market
     Storiant
By: Impetus     Published Date: Mar 15, 2016
Streaming analytics platforms provide businesses a method for extracting strategic value from data-in-motion in a manner similar to how traditional analytics tools operate on data-at rest. Instead of historical analysis, the goal with streaming analytics is to enable near real-time decision making by letting companies inspect, correlate and analyze data even as it flows into applications and databases from numerous different sources. Streaming analytics allows companies to do event processing against massive volumes of data streaming into the enterprise at high velocity.
Tags : impetus, guide to stream analytics, real time streaming analytics, streaming analytics, real time analytics, big data analytics
     Impetus
By: TIBCO     Published Date: Sep 02, 2014
In this Guide you will learn how predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes. It is important to adopt a predictive analytics solution that meets the specific needs of different users and skill sets from beginners, to experienced analysts, to data scientists.
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     TIBCO
By: IBM     Published Date: Nov 14, 2014
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: IBM     Published Date: Feb 13, 2015
In the age of "big data," it's essential for any organization to know how to analyze and manage their ever increasing stores of information. Packed with everything you need to know about Hadoop analytics, this handy guide provides you with a solid understanding of the critical big data concepts and trends, and suggests ways for you to revolutionize your business operations through the implementation of cost-effective, high performance Hadoop technology.
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     IBM
By: Dell and Intel®     Published Date: Aug 24, 2015
In today’s digitally driven world, the success of a business is increasingly tied to its ability to extract value from data. Exploiting the untapped value of your data is now the pathway to success. By putting data-driven decision making at the heart of the business, your organization can harness a wealth of information to gain an unparalleled competitive advantage. In a future-ready enterprise, you must make a fundamental shift from a focus on technology to a strategic business focus. Data-driven insights can guide everything from the formulation of top-level corporate strategies to connected devices that monitor and enable immediate critical decisions, to the creation of personalized customer interactions. Data is the foundation for enabling business transformation and innovation.
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     Dell and Intel®
By: Revolution Analytics     Published Date: May 09, 2014
As the primary facilitator of data science and big data, machine learning has garnered much interest by a broad range of industries as a way to increase value of enterprise data assets. Through techniques of supervised and unsupervised statistical learning, organizations can make important predictions and discover previously unknown knowledge to provide actionable business intelligence. In this guide, we’ll examine the principles underlying machine learning based on the R statistical environment. We’ll explore machine learning with R from the open source R perspective as well as the more robust commercial perspective using Revolution Analytics Enterprise (RRE) for big data deployments.
Tags : revolution analytics, data science, big data, machine learning
     Revolution Analytics
By: Adaptive Computing     Published Date: Feb 27, 2014
Big data applications represent a fast-growing category of high-value applications that are increasingly employed by business and technical computing users. However, they have exposed an inconvenient dichotomy in the way resources are utilized in data centers. Conventional enterprise and web-based applications can be executed efficiently in virtualized server environments, where resource management and scheduling is generally confined to a single server. By contrast, data-intensive analytics and technical simulations demand large aggregated resources, necessitating intelligent scheduling and resource management that spans a computer cluster, cloud, or entire data center. Although these tools exist in isolation, they are not available in a general-purpose framework that allows them to inter operate easily and automatically within existing IT infrastructure.
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     Adaptive Computing
By: KPMG     Published Date: Jun 10, 2019
Getting complex decisions right across complicated operational networks is the key to optimum performance. Find out how one of the UK’s biggest bus operators is using data and analytics to make better decisions and optimise the use of resources across their network. Read this story to discover: • how data and analytics can transform operational performance • the benefits of using decision-support tools in the middle office • key lessons for getting your plans for digital transformation right.
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     KPMG
By: Infinidat EMEA     Published Date: May 14, 2019
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
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     Infinidat EMEA
By: Zaloni     Published Date: Apr 23, 2019
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
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     Zaloni
By: Cisco EMEA     Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
Tags : big data, analytics, virtualization, cloudera, ibm, sas, sap, splunk
     Cisco EMEA
By: Cisco EMEA     Published Date: Mar 05, 2018
The competitive advantages and value of BDA are now widely acknowledged and have led to the shifting of focus at many firms from “if and when” to “where and how.” With BDA applications requiring more from IT infrastructures and lines of business demanding higher-quality insights in less time, choosing the right infrastructure platform for Big Data applications represents a core component of maximizing value. This IDC study considered the experiences of firms using Cisco UCS as an infrastructure platform for their BDA applications. The study found that Cisco UCS contributed to the strong value the firms are achieving with their business operations through scalability, performance, time to market, and cost effectiveness. As a result, these firms directly attributed business benefits to the manner in which Cisco UCS is deployed in the infrastructure.
Tags : big data, analytics, cisco, value, business, enterprise
     Cisco EMEA
By: Hewlett Packard Enterprise     Published Date: May 11, 2018
If your business is like most, you are grappling with data storage. In an annual Frost & Sullivan survey of IT decision-makers, storage growth has been listed among top data center challenges for the past five years.2 With businesses collecting, replicating, and storing exponentially more data than ever before, simply acquiring sufficient storage capacity is a problem. Even more challenging is that businesses expect more from their stored data. Data is now recognized as a precious corporate asset and competitive differentiator: spawning new business models, new revenue streams, greater intelligence, streamlined operations, and lower costs. Booming market trends such as Internet of Things and Big Data analytics are generating new opportunities faster than IT organizations can prepare for them.
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     Hewlett Packard Enterprise
By: SAP     Published Date: May 18, 2014
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
Tags : 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
     SAP
By: SAP     Published Date: May 18, 2014
Download this whitepaper to learn the results of this latest exploration of the emerging world of in-memory database technologies.
Tags : 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
     SAP
By: SAP     Published Date: May 18, 2014
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
Tags : 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
     SAP
By: SAP     Published Date: May 18, 2014
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
Tags : 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
     SAP
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