enterprise data

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By: NetApp     Published Date: Dec 13, 2013
FlexPod Select with Hadoop delivers enterprise class Hadoop with validated, pre-configured components for fast deployment, higher reliability and smoother integration with existing applications and infrastructure.  These technical reference architectures optimize storage, networking, and servers with Cloudera and Hortonworks distributions of Hadoop. Leverage FlexPod Select with Hadoop to help store, manage, process and perform advanced analytics on your multi-structured data.   Tuning parameters, optimization techniques among other Hadoop cluster guidelines  are provided.
Tags : flexpod with hadoop, enterprise data, storage infrastructure, massive amounts of data
     NetApp
By: NetApp     Published Date: -
Enterprise data is growing rapidly - reaching multiple petabytes of data or even billions of files for many organizations. To maximize the business value of this data, enterprises need a storage infrastructure to store, manage, and retrieve a massive amount of data. This ebook shows you how to address large content repository challenges with object storage. You'll learn how to effectively address long-term retention policies, find and retrieve content quickly from long-term repositories and using object storage efficiently.
Tags : object storage, storage infrastructure
     NetApp
By: IBM     Published Date: Sep 02, 2014
Advanced analytics strategies yield the greatest benefits in terms of improving patient and business outcomes when applied across the entire healthcare ecosystem. But the challenge of collaborating across organizational boundaries in order to share information and insights is daunting to many stakeholders. In this worldwide survey of 555 healthcare providers, payers and life sciences organizations, you will learn the importance of implementing collaborative analytics strategies that: Manage, integrate and interpret data generated at all stages of the healthcare value chain Achieve the right balance of skills in order to translate data into actionable insights Focus on executive sponsorship and enterprise-wide adoption with metrics to measure and track success Position yourself to harness data, create and share insights, make informed decisions, and improve the performance of the entire healthcare ecosystem in which you operate.
Tags : ibm, analytics acrosss ecosystem
     IBM
By: IBM     Published Date: Sep 02, 2014
Research teams using next-generation sequencing (NGS) technologies face the daunting challenge of supporting compute-intensive analysis methods against petabytes of data while simultaneously keeping pace with rapidly evolving algorithmic best practices. NGS users can now solve these challenges by deploying the Accelrys Enterprise Platform (AEP) and the NGS Collection on optimized systems from IBM. Learn how you can benefit from the turnkey IBM Application Ready Solution for Accelrys with supporting benchmark data.
Tags : ibm, accelrys, turnkey ngs solution
     IBM
By: IBM     Published Date: Sep 02, 2014
With tougher regulations and continuing market volatility, financial firms are moving to active risk management with a focus on counterparty risk. Firms are revamping their risk and trading practices from top to bottom. They are adopting new risk models and frameworks that support a holistic view of risk. Banks recognize that technology is critical for this transformation, and are adding state-of-the-art enterprise risk management solutions, high performance data and grid management software, and fast hardware. Join IBM Algorithmics and IBM Platform Computing to gain insights on this trend and on technologies for enabling active "real-time" risk management.
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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: Sep 16, 2015
A fast, simple, scalable and complete storage solution for today’s data-intensive enterprise IBM Spectrum Scale is used extensively across industries worldwide. Spectrum Scale simplifies data management with integrated tools designed to help organizations manage petabytes of data and billions of files—as well as control the cost of managing these ever-growing data volumes.
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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: RedPoint     Published Date: Sep 22, 2014
Enterprises can gain serious traction by taking advantage of the scalability, processing power and lower costs that Hadoop 2.0/YARN offers. YARN closes the functionality gap by opening Hadoop to mature enterprise-class data management capabilities. With a lot of data quality functionality left outside of Hadoop 1, and a lot of data inside HDFS originating outside the enterprise, the quality of the data residing in the Hadoop cluster is sometimes as stinky as elephant dung. Some of the topics discussed in this paper include: • The key features, benefits and limitations of Hadoop 1.0 • The benefit of performing data standardization, identity resolution, and master data management inside of Hadoop. • The transformative power of Hadoop 2.0 and its impact on the speed and cost of accessing, cleansing and delivering high-quality enterprise data. Download this illuminating white paper about what YARN really means to the world of big data management.
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     RedPoint
By: MEMSQL     Published Date: Apr 12, 2016
The pace of data is not slowing. Applications of today are built with infinite data sets in mind. As these real-time applications become the norm, and batch processing becomes a relic of the past, digital enterprises will implement memory-optimized, distributed data systems to simplify Lambda Architectures for real-time data processing and exploration.
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     MEMSQL
By: EMC     Published Date: Jun 13, 2016
IDC believes that EMC Isilon is indeed an easy to operate, highly scalable and efficient Enterprise Data Lake Platform. IDC validated that a shared storage model based on the Data Lake can in fact provide enterprise-grade service-levels while performing better than dedicated commodity off-the-shelf storage for Hadoop workloads.
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     EMC
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: MapR Technologies     Published Date: Sep 04, 2013
Enterprises are faced with new requirements for data. We now have big data that is different from the structured, cleansed corporate data repositories of the past. Before, we had to plan out structured queries. In the Hadoop world, we don’t have to sort data according to a predetermined schema when we collect it. We can store data as it arrives and decide what to do with it later. Today, there are different ways to analyze data collected in Hadoop—but which one is the best way forward?
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     MapR Technologies
By: Dell and Intel®     Published Date: Sep 17, 2014
According to the 2014 IDG Enterprise Big Data research report, companies are intensifying their efforts to derive value through big data initiatives with nearly half (49%) of respondents already implementing big data projects or in the process of doing so in the future. Further, organizations are seeing exponential growth in the amount of data managed with an expected increas of 76% within the next 12-18 months. With growth there are opportunities as well as challenges. Among those facing the big data challenge are finance executives, as this extraordinary growth presents a unique opportunity to leverage data assets like never before. * Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
Tags : dell and intel, big data for finance
     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
Many enterprises are embracing Hadoop because of the unique business benefits it provides. But, until now, this rapidly evolving big data technology hadn’t always met enterprise security needs. In order to protect big data today, organizations must have solutions that address four key areas: authentication, authorization, audit and lineage, and compliant data protection.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
New technologies help decision makers gain insights from all types of data - from traditional databases to high-visibility social media sources. Big data initiatives must ensure data is cost-effectively managed, shared by systems across the enterprise, and quickly and securely made available for analysis and action by line-of-business teams. In this article, learn how Dell working with Intel® helps IT leaders overcome the challenges of IT and business alignment, resource constraints and siloed environments through a comprehensive big data portfolio based on choice and flexibility, redefined economics and connected intelligence.
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     Dell and Intel®
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: snowflake     Published Date: Jun 09, 2016
Why Read This Report In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
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     snowflake
By: snowflake     Published Date: Jun 09, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises datacenters.
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     snowflake
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: BitStew     Published Date: May 26, 2016
The heaviest lift for an industrial enterprise is data integration, the Achilles’ heel of the Industrial Internet of Things (IIoT). Companies are now recognizing the enormous challenge involved in supporting Big Data strategies that can handle the data that is generated by information systems, operational systems and the extensive networks of old and new sensors. To compound these issues, business leaders are expecting data to be captured, analyzed and used in a near real-time to optimize business processes, drive efficiency and improve profitability. However, integrating this vast amount of dissimilar data into a unified data strategy can be overwhelming for even the largest organizations. Download this white paper, by Bit Stew’s Mike Varney, to learn why a big data solution will not get the job done. Learn how to leverage machine intelligence with a purpose-built IIoT platform to solve the data integration problem.
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     BitStew
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: VMware     Published Date: Oct 02, 2018
VMware surveyed approximately 200 senior IT leaders at enterprise, commercial, and SMB organizations worldwide to shed light on the evolving role of IT. In the face of increased pressures, IT oganizations are needing to accelerate development and delivery of applications that power faster time-to-market for the business. In this paper, we chart the progress of CIOs, CTOs and other IT leaders and discuss the top challenges they face in building more nimble, responsive organizations to support their business objectives. We also show how data centre modernization can help organizations deliver the agility required to meet ever-increasing demands for faster innovation through a flexible, service-oriented IT model that employs both private and public clouds. Submit the form to discover the access this latest paper and set the pace in today’s digital economy.
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     VMware
By: Attunity     Published Date: Nov 15, 2018
Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems. To realize these benefits, enterprises need to understand how this critical technology works, why it’s needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC. Read this book to understand: ? The rise of data lake, streaming and cloud platforms ? How CDC works and enables these architectures ? Case studies of leading-edge enterprises ? Planning and implementation approaches
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     Attunity
By: Lenovo - India     Published Date: Oct 11, 2018
While many organizations have acknowledged the importance of digitally transforming their businesses and processes to deliver speed and agility, most are just starting their cloud journey. Cloud-in-a-Box delivers an enterprise cloud platform with a mix of on- and off-premises private cloud and public cloud, with orchestration between them. This will provide the organization with the ability to avoid vendor lock-in, find the optimal cloud services for a particular business or technical need, and increase redundancy. Download IDC Infobrief on “Cloud-in-a-Box” to understand: • How you can deliver the different functions of a data center and meet business requirements by deploying a “Cloud-in-a-Box.” • Understand how industries such as financial services, manufacturing and healthcare can benefit from “Cloud-in-a-Box” technology. • Self-discovery questions and focus areas that every organization needs to go through for successful cloud deployment.
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     Lenovo - India
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