analytic workload

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By: IBM     Published Date: Jun 05, 2014
Are infrastructure limitations holding you back? - Users struggling with access to sufficient compute resources - Resources tapped out during peak demand times - Lack of budget, space or power to the environment IBM recently announced a new Platform Computing cloud service delivering hybrid cloud optimized for analytics and technical computing applications. The offering provides: - Ready to run IBM Platform LSF & Symphony clusters in the cloud - Seamless workload management, on premise and in the cloud - 24x7 cloud operation technical support - Dedicated, isolated physical machines for complete security Join us for this brief 20-minute webcast to learn how IBM offers a complete end-to-end hybrid cloud solution that may be key to improving your organization’s effectiveness and expediting time to market for your products.
Tags : ibm
     IBM
By: IBM     Published Date: Aug 27, 2014
Are infrastructure limitations holding you back? Users struggling with access to sufficient compute resources Resources tapped out during peak demand times Lack of budget, space or power to the environment IBM recently announced a new Platform Computing cloud service delivering hybrid cloud optimized for analytics and technical computing applications. The offering provides: Ready to run IBM Platform LSF & Symphony clusters in the cloud Seamless workload management, on premise and in the cloud 24x7 cloud operation technical support Dedicated, isolated physical machines for complete security Join us for this brief 20-minute webcast to learn how IBM offers a complete end-to-end hybrid cloud solution that may be key to improving your organization’s effectiveness and expediting time to market for your products.
Tags : ibm, hybrid cloud
     IBM
By: IBM     Published Date: Sep 16, 2015
6 criteria for evaluating a high-performance cloud services providers Engineering, scientific, analytics, big data and research workloads place extraordinary demands on technical and high-performance computing (HPC) infrastructure. Supporting these workloads can be especially challenging for organizations that have unpredictable spikes in resource demand, or need access to additional compute or storage resources for a project or to support a growing business. Software Defined Infrastructure (SDI) enables organizations to deliver HPC services in the most efficient way possible, optimizing resource utilization to accelerate time to results and reduce costs. SDI is the foundation for a fully integrated environment, optimizing compute, storage and networking infrastructure to quickly adapt to changing business requirements, and dynamically managing workloads and data, transforming a s
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     IBM
By: IBM     Published Date: Sep 16, 2015
Are you trying to support more variable workloads than your environment can handle? Can you benefit from a high performance cluster, but do not have the budget or resources to deploy and manage technical computing infrastructure? Are you running out of data center space but still need to grow your compute capacity? If you want answers to any of these questions, then please join us for an informative webinar describing the advantages and pitfalls of relocating a high performance workload to the cloud. View this webinar to learn: - Why general purpose clouds are insufficient for technical computing analytics and Hadoop workloads; - How high performance clouds can improve your profitability and give you a competitive edge; - How to ensure that your cloud environment is secure; - How to evaluate which applications are suitable for a hybrid or public cloud environment; - How to get started and choose a service provider.
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     IBM
By: IBM     Published Date: May 20, 2015
Are you trying to support more variable workloads than your environment can handle? Can you benefit from a high performance cluster, but do not have the budget or resources to deploy and manage technical computing infrastructure? Are you running out of data center space but still need to grow your compute capacity? If you want answers to any of these questions, then please join us for an informative webinar describing the advantages and pitfalls of relocating a high performance workload to the cloud. View this webinar to learn: - Why general purpose clouds are insufficient for technical computing analytics and Hadoop workloads; - How high performance clouds can improve your profitability and give you a competitive edge; - How to ensure that your cloud environment is secure; - How to evaluate which applications are suitable for a hybrid or public cloud environment; - How to get started and choose a service provider.
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     IBM
By: IBM     Published Date: May 20, 2015
Are infrastructure limitations holding you back? Users struggling with access to sufficient compute resources Resources tapped out during peak demand times Lack of budget, space or power to the environment IBM recently announced a new Platform Computing cloud service delivering hybrid cloud optimized for analytics and technical computing applications. The offering provides: Ready to run IBM Platform LSF & Symphony clusters in the cloud Seamless workload management, on premise and in the cloud 24x7 cloud operation technical support Dedicated, isolated physical machines for complete security Join us for this brief 20-minute webcast to learn how IBM offers a complete end-to-end hybrid cloud solution that may be key to improving your organization’s effectiveness and expediting time to market for your products.
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     IBM
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
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: SAS     Published Date: Sep 05, 2019
Organizations are charging ahead with investments in cloud and analytics to deliver agility, scalability and cost savings. With computing power advancements and continuous growth of data, cloud provides the elastic workloads and flexibility required for modern business. However, the environment of flexibility and choice that cloud provides also creates complexity and challenges. In this white paper, learn how organizations are applying expertise and using the latest methods to move analytics to the cloud, including: • Why are organizations moving analytic work to the cloud? • What are the key challenges and misconceptions? • How do IT leaders provide choice while maintaining control?
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     SAS
By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
Tags : database usage, database management, server usage, data protection
     Hewlett Packard Enterprise
By: Dell Server     Published Date: Aug 07, 2018
Today’s digital businesses are managed using critical business analyses that provide far greater insight into the business and how to maximize results. However, these high-value applications that use the latest software tools demand far more from IT infrastructure, as they utilize an order of magnitude more data and demand more compute resources than legacy applications. Legacy systems are no longer capable of meeting the present and future needs of the organization. Forward-thinking IT organizations are developing new infrastructure strategies to better support high-value analytics workloads with Dell EMC PowerEdge Servers powered by Intel® Xeon® Platinum processor.
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     Dell Server
By: Avi Networks     Published Date: Mar 06, 2019
OpenShift-Kubernetes offers an excellent automated application deployment framework for container-based workloads. Services such as traffic management (load balancing within a cluster and across clusters/regions), service discovery, monitoring/analytics, and security are a critical component of an application deployment framework. Enterprises require a scalable, battle-tested, and robust services fabric to deploy business-critical workloads in production environments. This whitepaper provides an overview of the requirements for such application services and explains how Avi Networks provides a proven services fabric to deploy container based workloads in production environments using OpenShift- Kubernetes clusters.
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     Avi Networks
By: TIBCO Software     Published Date: May 31, 2018
With data the new competitive battleground, businesses that take advantage of their data will be the leaders; those that do not will fall behind. But gaining an advantage is a more difficult technical challenge than ever because your business requirements are ever-changing, your analytic workloads are exploding, and your data is now widely-distributed across on-premises, big data, the Internet of Things, and the Cloud. TIBCO® Data Virtualization is data virtualization software that lets you integrate data at big data scale with breakthrough speed and cost effectiveness. With TIBCO Data Virtualization, you can build and manage virtualized views / data services that access, transform, and deliver the data your business requires to accelerate revenue, reduce costs, lessen risk, improve compliance, and more.
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     TIBCO Software
By: CA Technologies     Published Date: Jul 20, 2017
e-book which lays out the case for machine learning and artificial intelligence for mainframe operational analytics. The mainframe is now part of a highly complex connected ecosystem driving trillions of mobile and web transactions critical to the functioning of the application economy. The emergence of new workloads and apps on the mainframe means that the status quo isn’t enough when it comes to Mainframe management. IT professionals alone – whether mainframe skilled or not – simply can’t keep up with the onslaught of performance alerts, false alarms. Machine learning deliver mainframe intelligence a more proactive and automated approach to handle this challenge.
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     CA Technologies
By: Veritas     Published Date: Jan 04, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands. You will learn: The key trends that are driving change in the digital business The most common causes of data loss in tomorrow’s multi-cloud data centers How to protect an increasingly diverse environment with minimal operational overhead
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     Veritas
By: TIBCO Software     Published Date: Aug 13, 2018
Despite being knowledgeable about their industry and experienced in running their organizations, the majority of business users lack expertise in analytics and visualization techniques—but that doesn't stop them from wanting to have a go. But making tools easier and more widely accessible is only part of the answer. A better approach is to work both sides of the gap. To make tools that can empower business users to discover and unlock value in their data—and that extend capabilities for experts, so they can share the analytics workload, improve efficiency, and focus on higher level work.
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     TIBCO Software
By: TIBCO Software     Published Date: May 31, 2018
Ask the average business user what they know about Business Intelligence (BI) and data analytics, and most will claim to understand the concepts. Few, however, will profess to know how analytics works or to have the skills needed to put it into practice. Despite being knowledgeable about their industry and experienced in running their organizations, the majority of business users lack expertise in analytics and visualization techniques—but that doesn’t stop them from wanting to have a go. This situation has led to ease of use and accessibility becoming the main focus for recent updates from all the leading BI vendors—but making tools easier and more widely accessible is only part of the answer. A better approach is to work both sides of the gap. To make tools that can empower business users to discover and unlock value in their data—and that extend capabilities for experts, so they can share the analytics workload, improve efficiency, and focus on higher level work. Unfortunately, the
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     TIBCO Software
By: IBM     Published Date: Jul 21, 2016
IBM's recently released DB2 version 11.1 for Linux, Unix and Windows (LUW) is a hybrid database that IBM says can handle transactional and analytic workloads thanks to its BLU Acceleration technology, which features an in-memory column store for analytical workloads that can scale across a massively parallel cluster.
Tags : ibm, db2. analytics, mpp, data wharehousing
     IBM
By: IBM     Published Date: Apr 19, 2018
This paper presents a cost/benefit case for two industry-leading database platforms for analytics workloads.
Tags : db2, data migration, ibm, oracle
     IBM
By: IBM     Published Date: Aug 08, 2018
An IBM Cloud configuration completed a big data analytics workload in less time and with greater throughput than an AWS solution
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     IBM
By: Juniper Networks     Published Date: Feb 05, 2018
Innovative data-driven strategies are enabling organizations to connect with customers and increase operational efficiency as never before. These new initiatives are built on a multitude of applications, such as big-data analytics, supply chain, and factory automation. On average, organizations are now 53% digital as they create new ways of operating and growing their businesses, according to the Computerworld 2017 Forecast Study. As part of this transformation, enterprises rely increasingly on multivendor, multicloud environments that mix on-premise, private, and public cloud services and workloads. This shift is causing enterprises to increase network capacity; 55% of enterprises in the Computerworld study expect to add network bandwidth in the next 12 months.
Tags : security, automation, savings, technology, cloud
     Juniper Networks
By: IBM APAC     Published Date: Oct 16, 2018
Modern AI, HPC and analytics workloads are driving an ever-growing set of data intensive challenges that can only be met with accelerated infrastructure. Designed for the AI era and architected for the modern analytics and AI workloads, Power Systems AC922 delivers unprecedented speed for the AI Era like up to 5.6 times as much bandwidth, which results from The only architecture enabling NVLink between CPUs and GPUs a variety of next-generation I/O architectures: PCIe gen4, CAPI 2.0, OpenCAPI and NVLINK. Proven simplified deep-learning deployment and AI performance
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     IBM APAC
By: Teradata     Published Date: Feb 26, 2013
This survey and research report discusses shifts in the data management landscape and the movement to align data with operational and analytical workloads creating the best possible unified data architecture platform. Read on to learn more.
Tags : big data, survey report, data management, landscape, movement, align data, operational and analytics workloads, business intelligence
     Teradata
By: BMC ASEAN     Published Date: Dec 18, 2018
400+ IT professionals reveal what’s next for workload automation How are today’s leading companies using workload automation to drive their most important IT initiatives? Find out in this detailed report summary from analyst firm Enterprise Management Associates (EMA). You’ll learn how the role of workload automation is evolving, including: The rise of predictive analytics in WLA Surprising stats on the frequency and ease of migration How big data and cloud impact WLA The use of containers and micro services architectures Workload automation is changing fast. Keep up with the latest analyst research – download the report.
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     BMC ASEAN
By: Gigaom     Published Date: Sep 16, 2019
We’ve heard it before. A data warehouse is a place for formally-structured, highly-curated data, accommodating recurring business analyses, whereas data lakes are places for “raw” data, serving analytic workloads, experimental in nature. Since both conventional and experimental analysis is important in this data-driven era, we’re left with separate repositories, siloed data, and bifurcated skill sets. Or are we? In fact, less structured data can go into your warehouse, and since today’s data warehouses can leverage the same distributed file systems and cloud storage layers that host data lakes, the warehouse/lake distinction’s very premise is rapidly diminishing. In reality, business drivers and business outcomes demand that we abandon the false dichotomy and unify our data, our governance, our analysis, and our technology teams. Want to get this right? Then join us for a free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest, Dav
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     Gigaom
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