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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: IBM     Published Date: Sep 02, 2014
Life Sciences organizations need to be able to build IT infrastructures that are dynamic, scalable, easy to deploy and manage, with simplified provisioning, high performance, high utilization and able to exploit both data intensive and server intensive workloads, including Hadop MapReduce. Solutions must scale, both in terms of processing and storage, in order to better serve the institution long-term. There is a life cycle management of data, and making it useable for mainstream analyses and applications is an important aspect in system design. This presentation will describe IT requirements and how Technical Computing solutions from IBM and Platform Computing will address these challenges and deliver greater ROI and accelerated time to results for Life Sciences.
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     IBM
By: Splice Machine     Published Date: Feb 13, 2014
Hadoop: Moving Beyond the Big Data Hype Let’s face it. There is a lot of hype surrounding Big Data and adoop, the defacto Big Data technology platform. Companies want to mine and act on massive data sets, or Big Data, to unlock insights that can help them improve operational efficiency, delight customers, and leapfrog their competition. Hadoop has become popular to store massive data sets because it can distribute them across inexpensive commodity servers. Hadoop is fundamentally a file system (HDFS or Hadoop Distributed File System) with a specialized programming model (MapReduce) to process the data in the files. Big Data has not lived up to expectations so far, partly because of limitations of Hadoop as a technology.
Tags : sql-on-hadoop® evaluation guide, splice machine, adoop
     Splice Machine
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: Dell and Intel®     Published Date: Jun 18, 2015
The rapid evolution of big data technology in the past few years has changed forever the pursuit of scientific exploration and discovery. Along with traditional experiment and theory, computational modeling and simulation is a third paradigm for science. Its value lies in exploring areas of science in which physical experimentation is unfeasible and insights cannot be revealed analytically, such as in climate modeling, seismology and galaxy formation. More recently, big data has been called the “fourth paradigm" of science. Big data can be observed, in a real sense, by computers processing it and often by humans reviewing visualizations created from it. In the past, humans had to reduce the data, often using techniques of statistical sampling, to be able to make sense of it. Now, new big data processing techniques will help us make sense of it without traditional reduction
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     Dell and Intel®
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: RedPoint     Published Date: Sep 22, 2014
The emergence of YARN for the Hadoop 2.0 platform has opened the door to new tools and applications that promise to allow more companies to reap the benefits of big data in ways never before possible with outcomes possibly never imagined. By separating the problem of cluster resource management from the data processing function, YARN offers a world beyond MapReduce: less encumbered by complex programming protocols, faster, and at a lower cost. Some of the topics discussed in this paper include: • Why is YARN important for realizing the power of Hadoop for data integration, quality and management? • Benchmark results of MapReduce vs. Pig vs. visual “data flow” design tools • The 3 key features of YARN that solve the complex problems that prohibit businesses from gaining maximum benefit from Hadoop. Download this paper to learn why the power of Hadoop 2.0 lies in enabling applications to run inside Hadoop, without the constraints of MapReduce.
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     RedPoint
By: GridGain     Published Date: Sep 24, 2014
In-memory computing (IMC) is an emerging field of importance in the big data industry. It is a quickly evolving technology, seen by many as an effective way to address the proverbial 3 V’s of big data—volume, velocity, and variety. Big data requires ever more powerful means to process and analyze growing stores of data, being collected at more rapid rates, and with increasing diversity in the types of data being sought—both structured and unstructured. In-memory computing’s rapid rise in the marketplace has the big data community on alert. In fact, Gartner picked in-memory computing as one of the Top Ten Strategic Initiatives.
Tags : gridgain, in memory computing, big data industry, 3v's of big data-volume
     GridGain
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: Intel     Published Date: Aug 06, 2014
Powering Big Data Workloads with Intel® Enterprise Edition for Lustre* software The Intel® portfolio for high-performance computing provides the following technology solutions: • Compute - The Intel® Xeon processor E7 family provides a leap forward for every discipline that depends on HPC, with industry-leading performance and improved performance per watt. Add Intel® Xeon Phi coprocessors to your clusters and workstations to increase performance for highly parallel applications and code segments. Each coprocessor can add over a teraflops of performance and is compatible with software written for the Intel® Xeon processor E7 family. You don’t need to rewrite code or master new development tools. • Storage - High performance, highly scalable storage solutions with Intel® Lustre and Intel® Xeon Processor E7 based storage systems for centralized storage. Reliable and responsive local storage with Intel® Solid State Drives. • Networking - Intel® True Scale Fabric and Networking technologies – Built for HPC to deliver fast message rates and low latency. • Software and Tools: A broad range of software and tools to optimize and parallelize your software and clusters. Further, Intel Enterprise Edition for Lustre software is backed by Intel, the recognized technical support providers for Lustre, and includes 24/7 service level agreement (SLA) coverage.
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     Intel
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: Apr 02, 2015
In this Guide we have delivered the case for the benefits of big data technology applied to the needs of the manufacturing industry. In demonstrating the value of big data, we included: • An overview of how manufacturing can benefit from the big data technology stack • An overview of how manufacturing can benefit from the big data technology stack • A high-level view of common big data pain points for manufacturers • A detailed analysis of big data technology for manufacturers • A view as to how manufacturers are going about big data adoption • A proven case study with: Omneo • Dell PowerEdge servers with Intel® Xeon® processors
Tags : dell, intel, big data, manufacturing, technology stack, pain points, big data adoption, omneo
     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
Business need: Merkle needed a scalable, cost-effective way to capture and analyze large amounts of structured and unstructured consumer data for use in developing better marketing campaigns for clients. Solution: The company deployed a Dell and HadoopTM cluster based on Dell and Intel® technologies to support a new big data insight solution that gives clients a unified view of customer data. Benefits: [bullets for the below points] • Partnership with Dell and Intel® leads to new big data solution • Cluster supports the Foundational Marketing Platform, a new data insight solution • Merkle can find patterns in big data and create analytical models that anticipate consumer behavior • Organization cuts costs by 60 percent and boosts processing speeds by 10 times • Solution provides scalability and enables innovation
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries. The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell and its partners Cloudera® and Intel®.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
To extract value from an ever-growing onslaught of data, your organization needs next-generation data management, integration, storage and processing systems that allow you to collect, manage, store and analyze data quickly, efficiently and cost-effectively. That’s the case with Dell| Cloudera® Apache™ Hadoop® solutions for big data. These solutions provide end-to-end scalable infrastructure, leveraging open source technologies, to allow you to simultaneously store and process large datasets in a distributed environment for data mining and analysis, on both structured and unstructured data, and to do it all in an affordable manner.
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     Dell and Intel®
By: Kx Systems     Published Date: Jan 16, 2015
?Kdb+ is a column-based relational database with extensive in-memory capabilities, developed and marketed by Kx Systems. Like all such products, it is especially powerful when it comes to supporting queries and analytics. However, unlike other products in this domain, kdb+ is particularly good (both in terms of performance and functionality) at processing, manipulating and analysing data (especially numeric data) in real-time, alongside the analysis of historical data. Moreover, it has extensive capabilities for supporting timeseries data. For these reasons Kx Systems has historically targeted the financial industry for trading analytics and black box trading based on real-time and historic data, as well as realtime risk assessment; applications which are particularly demanding in their performance requirements. The company has had significant success in this market with over a hundred major financial institutions and hedge funds deploying its technology. In this paper, however, we wa
Tags : kx systems, kdb+, relational database
     Kx Systems
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: 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: Here Technologies     Published Date: Apr 02, 2019
In today’s interconnected world you need to future-proof the integrity and reputation of your business. Your company’s network remains the Achilles heel of the whole enterprise because once compromised, your company’s reputation is compromised as well. Banks, credit card brands, payment processors, and e-commerce companies regularly launch new products and services that have new, unforeseen fraud risk factors. HERE can provide an additional layer of security and safeguard your company's reputation. As one of the world’s leading location platforms in 2018, HERE shares insights and solutions to preventing mobile payment fraud, credit card fraud and identity fraud.
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     Here Technologies
By: IBM APAC     Published Date: Jul 19, 2019
AI applications and especially deep learning systems are extremely demanding and require powerful parallel processing capabilities. IDC research shows that, in terms of core capacity, a large gap between actual and required CPU capability will develop in the next several years. IDC is seeing the worldwide market for accelerated servers grow to $25.6 billion in 2022, with a 31.6% CAGR. Indeed, this market is growing so fast that IDC is forecasting that by 2021,12% of worldwide server value will be from accelerated compute. Download this IDC report to find out why organizations like yours will need to make decisions about replacing existing general-purpose hardware or supplementing it with hardware dedicated to AI-specific processing tasks.
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     IBM APAC
By: Entrust Datacard     Published Date: Jul 23, 2019
Advanced key and certificate management enables the use of digital credentials even in the most demanding of security environments. Such solutions enable users, regardless of whether they are internal or external to their network, to benefit from both basic and enhanced capabilities in a consistent and secure manner. This document was created to assist organizations in the selection of the best PKI solution to meet their business and security needs. It outlines key questions to be considered during the selection process to ensure the aforementioned requirements are addressed. This is not intended to be an exhaustive list. It is meant as a starting place to assist you in your review process. Introduction
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     Entrust Datacard
By: Schneider Electric     Published Date: Jul 01, 2019
This eGuide examines how the IIoT, big data, and augmented reality are enabling a new era of cost reduction, asset management, and worker safety in water and wastewater processing.
Tags : augmented reality, big data, water
     Schneider Electric
By: Juniper Networks     Published Date: Jul 31, 2019
As the number and severity of cyberattacks continue to grow with no end in sight, cybersecurity teams are implementing new tools and processes to combat these emerging threats. However, the oneoverriding requirement for meeting this challenge is improved speed. Whether it’s speed of detection, speed of remediation or other processes that now need to be completed faster, the ability to do things quickly is key to effective cybersecurity. The reason why speed is essential is simple: As the dwell time for malware increases, the lateral spread of an attack broadens, the number of potentially breached files expands, and the difficulty in remediating the threat increases. And the stealthy nature of many of the newer threats makes finding them faster?before they become harder to detect?a critical focus in reducing the impact of an intrusion. These requirements make it essential that security operations centers (SOCs) can complete their activities far more quickly, both now and moving forwa
Tags : application management, application performance, network infrastructure, network management, network security
     Juniper Networks
By: Juniper Networks     Published Date: Jul 31, 2019
The Security Operations Center (SOC) is the first line of defense against cyber attacks. They are charged with defending the business against the many new and more virulent attacks that occur all day, every day. And the pressure on the SOC is increasing. Their work is more important, as the cost of data breaches are now substantial. The Ponemon Institute’s “2017 Cost of Data Breach Study” says the average cost of an incursion is $3.62 million. The study also says larger breaches are occurring, with the average breach impacting more than 24,000 records. And with new regulations such as the EU’s General Data Protection Requirement (GDPR) putting stiff financial penalties on breaches of personal data, the cost of a breach can have material impact on the financial results of the firm. This trend toward increasingly onerous statutory demands will continue, as the U.S. is now considering the Data Privacy Act, which will bring more scrutiny and accompanying penalties for breaches involving
Tags : application management, application performance, network infrastructure, network management, network security
     Juniper Networks
By: ServiceNow     Published Date: Aug 02, 2019
Organizations spend a huge amount of money on enterprise software. Software asset management (SAM) practitioners still wrestle with time?consuming, inaccurate manual processes hosted in spreadsheets or legacy SAM tools. Fortunately, next?generation IT asset management solutions are starting to replace traditional SAM tools.
Tags : servicenow, asset, management, sam, spreadsheets, enterprise
     ServiceNow
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