Enterprises are cobbling together all the pieces that make up a digital business - improved analytics, scalable architecture, automation, and more engaging customer experiences, just to name a few. At the same time, they're facing the ever-present threats of cyber attacks and data breaches.
Today’s organizations are becoming more innovative and dynamic by adopting mobility, IoT, analytics and cloud technologies. With this, come growing demands for network scale, agility and threat protection which call for an intent-based network. The Cisco® Digital Network Architecture (Cisco DNA) gives you comprehensive intent-based networking across your campus, branch and WAN with robust wired, wireless, and routing solutions.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
Intent-based networking is the difference between a network that needs continuous attention and one that simply understands what you need and makes it happen. It’s the difference between doing thousands of tasks manually and having an automated system that helps you focus on business goals.
Cisco DNA is the open, software-driven platform that turns vision into reality. Virtualization, automation, analytics, and cloud, all in one architecture.
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.
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.
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.
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
Transform your business with services that allow you to innovate faster, simplify operations, and reduce risk. Building on an open, software-driven approach that uses virtualization, automation, analytics, and cloud, our networkwide architecture prepares you to respond to new opportunities at digital speed.
The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence.
This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
The customer experience is critical in today’s fast-paced, demanding world. With so many options at the buyer’s fingertips and the rise of social sharing media, one bad customer experience can haunt an organization. According to” The Forrester Wave™: Dynamic Case Management, Q1 2016” report, in order to improve customer experience, firms must tackle the incident process through smart handling of exceptions, applying analytics for context, and offering real-time and mobile interaction. Here is where dynamic case management (DCM) can be a competitive advantage and Forrester identified 14 of the most significant vendors across 21 criteria in this space with Pegasystems among vendors who lead the pack.
Download this Forrester Wave report to see the full 21-criteria evaluation of the dynamic case
management (DCM) market and gain insight into the 14 most significant software vendors in order to help enterprise architecture (EA) professi
The Scandinavian commercial vehicle manufacturer wanted better uptime and reduction of vehicle on road (VOR), and also provide real-time feedback and visibility to their customers. LTI helped streamline analytics to detect exceptions and create actionable and achievable high scalability with microservices and serverless architecture. Download full case study
Forward-looking organizations are looking to next-generation all-flash storage platforms to eliminate storage cost and performance barriers. Advancements in all-flash technology have led to remarkable priceperformance improvements in recent years. The latest all-flash solutions from HPE deliver breakthrough economics, speed and simplicity, while improving availability and data durability. All-flash storage can help you reduce TCO and boost the performance of traditional applications as well as accelerate the rollout of new initiatives like IoT, big data and analytics. But moving data to a new storage architecture introduces a variety of organizational and technical challenges.
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.
Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
Published By: Teradata
Published Date: Jan 30, 2015
This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.
Published By: Teradata
Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
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