analytic applications

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Published By: Pure Storage     Published Date: Jul 03, 2019
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude.
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Pure Storage
Published By: MicroStrategy     Published Date: Jun 11, 2019
The use of analytics has exploded across business, and the value it already has delivered has heightened executives' expectations. Now data can be processed in real time to meet a constantly widening range of analytic needs. How your organization utilizes them in the next decade will be essential to your success. These developments come at an opportune time. Organizations are being over-whelmed by the rivers of data generated by applications and systems on-premises or flowing in via the cloud. At the same time, the cost of computational power has declined dramatically, making it practical to apply analytics to and generate information on just about anything. But no advance comes without challenges. While the widespread availability of analytics has created seemingly valuable insights, executives and managers are finding that those insights are not easily linked to steps that will improve business outcomes or optimize actions. Furthermore, analytics are not always easy for line of b
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MicroStrategy
Published By: Citrix Systems     Published Date: Jun 04, 2019
Now that 72.3% of cloud users have a mix of on-prem/off-prem clouds, the networking game has changed. Traditional app delivery solutions can’t ensure reliable, secure access in a SaaS, multi-device, hybrid, and multi-cloud world. Get this solution brief to learn why a holistic strategy innately reduces complexities that otherwise would prohibit visibility and control in distributed architectures. The brief also explains: The Citrix Networking approach to delivering reliability and a high-quality experience How to ensure reliable access to apps at branch and remote locations while keeping costs low How to provide full visibility and analytics for your network, applications, users, and data
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Citrix Systems
Published 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
Published By: Zaloni     Published Date: Apr 24, 2019
Why your data catalog won’t deliver significant ROI According to Gartner, organizations that provide access to a curated catalog of internal and external data assets will derive twice as much business value from their analytics investments by 2020 than those that do not. That’s a ringing endorsement of data catalogs, and a growing number of enterprises seem to agree. In fact, the global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%. Why such large and intensifying demand for data catalogs? The primary driver is that many organizations are working to modernize their data platforms with data lakes, cloud-based data warehouses, advanced analytics and various SaaS applications in order to grow profitable digital initiatives. To support these digital initiatives and other business imperatives, organizations need more reliable, faster access to their data. However, modernizing data plat
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Zaloni
Published By: Citrix Systems     Published Date: Feb 14, 2019
"Now that 72.3% of cloud users have a mix of on-prem/off-prem clouds, the networking game has changed. Traditional app delivery solutions can’t ensure reliable, secure access in a SaaS, multi-device, hybrid, and multi-cloud world.  Get this solution brief to learn why a holistic strategy innately reduces complexities that otherwise would prohitib visibility and control in distributed architectures. The brief also explains: - The Citrix Networking approach to delivering reliability and a high-quality experience - How to ensure reliable access to apps at branch and remote locations while keeping costs low - How to provide full visibility and analytics for your network, applications, users, and data"
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Citrix Systems
Published By: Lenovo - APAC     Published Date: Feb 11, 2019
Asian ICT infrastructure investment is exploding as businesses review and modernise their data-centre architectures to keep up with the service demands of a growing and increasingly sophisticated population. Demand for cloud services, particularly to support big-data analytics initiatives, is driving this trend. Frost & Sullivan, for example, believes the Asia-Pacific cloud computing market will grow at 28.4 percent annually through 2022. Despite this growth, many businesses are also rapidly realising that public cloud is not the best solution for every need as theydo not always offer the same level of visibility, performance, and control as on-premises infrastructure.This reality is pushing many companies towards the middle ground of hybrid IT, in which applications and infrastructure are distributed across public cloud and self-managed data centre infrastructure. Read about Medical company Mutoh and how it took advantage of the latest technology.
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lenovodcg, nutanix, hyperconvergedinfrastructure, hci
    
Lenovo - APAC
Published By: Lenovo - APAC     Published Date: Jan 28, 2019
Asian ICT infrastructure investment is exploding as businesses review and modernise their data-centre architectures to keep up with the service demands of a growing and increasingly sophisticated population. Demand for cloud services, particularly to support big-data analytics initiatives, is driving this trend. Frost & Sullivan, for example, believes the Asia-Pacific cloud computing market will grow at 28.4 percent annually through 2022. Despite this growth, many businesses are also rapidly realising that public cloud is not the best solution for every need as they do not always offer the same level of visibility, performance, and control as on-premises infrastructure.This reality is pushing many companies towards the middle ground of hybrid IT, in which applications and infrastructure are distributed across public cloud and self-managed data centre infrastructure. Read about Medical company Mutoh and how it took advantage of the latest technology.
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lenovodcg, nutanix, hyperconvergedinfrastructure, hci
    
Lenovo - APAC
Published By: Lenovo - APAC     Published Date: Jan 28, 2019
Asian ICT infrastructure investment is exploding as businesses review and modernise their data-centre architectures to keep up with the service demands of a growing and increasingly sophisticated population. Demand for cloud services, particularly to support big-data analytics initiatives, is driving this trend. Frost & Sullivan, for example, believes the Asia-Pacific cloud computing market will grow at 28.4 percent annually through 2022. Despite this growth, many businesses are also rapidly realising that public cloud is not the best solution for every need as theydo not always offer the same level of visibility, performance, and control as on-premises infrastructure.This reality is pushing many companies towards the middle ground of hybrid IT, in which applications and infrastructure are distributed across public cloud and self-managed data centre infrastructure. Read about Medical company Mutoh and how it took advantage of the latest technology.
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lenovodcg, nutanix, hyperconvergedinfrastructure, hci
    
Lenovo - APAC
Published By: Visier     Published Date: Jan 25, 2019
John Schwarz founded Visier to address what he saw as the major failing of business intelligence and big data analytics. He had a front row seat in this market while leading Business Objects, the largest global business intelligence provider (acquired by SAP). John and co-founder Ryan Wong’s vision was to completely reinvent the approach to analytics, providing instant and complete, domain-specific applications to business leaders, answering their important strategic questions and leading them to adopt best management practices. Their applied business analytics project is working. Today, more than a hundred blue chip companies have selected Visier as their people strategy platform and are achieving incredible results. And that’s just the beginning.
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Visier
Published By: TIBCO Software GmbH     Published Date: Jan 15, 2019
Enterprises use data virtualization software such as TIBCO® Data Virtualization to reduce data bottlenecks so more insights can be delivered for better business outcomes. For developers, data virtualization allows applications to access and use data without needing to know its technical details, such as how it is formatted or where it is physically located. For developers, data virtualization helps rapidly create reusable data services that access and transform data and deliver data analytics with even heavylifting reads completed quickly, securely, and with high performance. These data services can then be coalesced into a common data layer that can support a wide range of analytic and applications use cases. Data engineers and analytics development teams are big data virtualization users, with Gartner predicting over 50% of these teams adopting the technology by 202
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TIBCO Software GmbH
Published By: Epicor     Published Date: Jan 09, 2019
Behind the scenes of the world’s leading industrial and manufacturing companies, a profound digital transformation is now underway .1 As with any profound transformation, the opportunities for competitive advantage and growth are enormous, but so are the challenges . How do you determine what your next digital transformation steps should be? How do you position your manufacturing business to take the next step—and the one after that? This eBook will help you answer these questions . In it we will: XX Introduce the role of visibility in digital transformation XX Consider three key trends that are shaping the future of manufacturing by delivering the visibility that enables transformation: – Growth of the Internet of Things (IoT) – The advance of analytics – The migration of applications to the cloud XX Identify the traits you need in your core systems to take advantage of these trends—giving you the visibility to identify and take the steps that will help transform your business into
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Epicor
Published By: SAS     Published Date: Jan 04, 2019
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, customer call centers, fraud detection, and recommendation engines to name just a few. Embedding analytics is certainly not new but has been gaining more attention recently as data volumes and the freq
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SAS
Published By: Splunk     Published Date: Nov 29, 2018
From protecting customer experience to preserving lines of revenue, IT operations teams face increasingly complex responsibilities and are responsible for preventing outages that could harm the organization. As a Splunk customer, your machine data platform empowers you to utilize machine learning to reduce MTTR. Discover how six companies utilize machine learning and AI to predict outages, protect business revenue and deliver exceptional customer experiences. Download the e-book to learn how: Micron Technology reduced number of IT incidents by more than 50% Econocom provides better customer service by centralizing once-siloed analytics, improving SLA performance and significantly reducing the number of events TransUnion combines machine data from multiple applications to create an end-to-end transaction flow
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predictive it, predictive it tools, predictive analytics for it, big data and predictive analytics
    
Splunk
Published By: SAS     Published Date: Nov 16, 2018
More account openings are taking place through digital devices and online, giving the access and anonymity fraudsters need to steal or fabricate identities. Since credit fraud often starts with a falsified application, it makes sense to have strong tools to monitor loans and credit lines from that point onward. This paper discusses analytics-driven methods for validating applications and spotting trouble at all three stages of bust-out fraud schemes.
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SAS
Published By: Cisco EMEA     Published Date: Nov 08, 2018
Digital transformation (DX) — a technology-driven business strategy — enables firms to gain or expand their competitive differentiation by embracing data-driven decision-making processes, whether for increasing operational efficiencies, developing new products and services, increasing customer satisfaction and retention, or getting a better intelligence on the market. Big Data and analytics (BDA) applications form the foundation for enterprisewide digital transformation initiatives. To find out more download this whitepaper today.
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Cisco EMEA
Published By: AWS     Published Date: Oct 30, 2018
Enhance the Availability & Performance of SAP HANA Apps Abstract: SAP HANA delivers powerful analytics capabilities that can help you improve business performance and drive digital transformation. You can more easily build reliable and performant SAP HANA-powered landscapes with SUSE Linux Enterprise Server for SAP Applications and Amazon Web Services (AWS). That’s because SUSE can help you achieve near zero downtime and sustain high-performance levels, while AWS delivers a broad and deep set of cloud services that are certified to fulfill the compute, memory, and storage requirements of SAP HANA.
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availability, performance, sap, hana, apps
    
AWS
Published By: Pure Storage     Published Date: Oct 09, 2018
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude
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Pure Storage
Published By: Group M_IBM Q418     Published Date: Oct 02, 2018
Organizations are faced with providing secure authentication, authorization, and Single Sign On (SSO) access to thousands of users accessing hundreds of disparate applications. Ensuring that each user has only the necessary and authorized permissions, managing the user’s identity throughout its life cycle, and maintaining regulatory compliance and auditing further adds to the complexity. These daunting challenges are solved by Identity and Access Management (IAM) software. Traditional IAM supports on-premises applications, but its ability to support Software-as-a-Service (SaaS)-based applications, mobile computing, and new technologies such as Big Data, analytics, and the Internet of Things (IoT) is limited. Supporting on-premises IAM is expensive, complex, and time-consuming, and frequently incurs security gaps. Identity as a Service (IDaaS) is an SaaS-based IAM solution deployed from the cloud. By providing seamless SSO integration to legacy on-premises applications and modern cloud-
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Group M_IBM Q418
Published By: Group M_IBM Q418     Published Date: Sep 10, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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Group M_IBM Q418
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technolog
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Amazon Web Services
Published By: AWS     Published Date: Sep 04, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technology
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AWS
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
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