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.
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform.
This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
The HX Data Platform uses a self-healing architecture that implements data replication for high availability, remediates hardware failures, and alerts your IT administrators so that problems can be resolved quickly and your business can continue to operate. Space-efficient, pointerbased snapshots facilitate backup operations, and native replication supports cross-site protection. Data-at-rest encryption protects data from security risks and threats. Integration with leading enterprise backup systems allows you to extend your preferred data protection tools to your hyperconverged environment.
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.
Published By: HPE Intel
Published Date: Jan 11, 2016
A famous architect once said that the origin of architecture was defined by the first time “two bricks were put together well.” And the more bricks you have, the more important putting them together well becomes. The same holds true in our data centers. The architecture of our compute, storage and network devices has always been important, but as the demands on our IT infrastructures grow, and we add more “bricks,” the architecture becomes more critical.
Today’s data centers are expected to deploy, manage, and report on different tiers of business applications, databases, virtual workloads, home
directories, and file sharing simultaneously. They also need to co-locate multiple systems while sharing power and energy. This is true for large as
well as small environments. The trend in modern IT is to consolidate as much as possible to minimize cost and maximize efficiency of data
centers and branch offices. HPE 3PAR StoreServ is highly efficient, flash-optimized storage engineered for the true convergence of block, file,
and object access to help consolidate diverse workloads efficiently. HPE 3PAR OS and converged controllers incorporate multiprotocol support
into the heart of the system architecture
Published By: Dell EMC
Published Date: Oct 13, 2016
Flexibility is important, since many future initiatives—big data, machine learning, emerging technologies, and new business directions—will be built on this cloud structure.
No matter what shape your cloud infrastructure takes, Dell EMC converged and hyper-converged platforms and innovations like Dell EMC VscaleTM Architecture, powered by Intel® Xeon® processors, deliver the pathways to scale-up and scale-out, today and tomorrow.
Published By: Dell EMC
Published Date: Nov 03, 2016
IT managers are struggling to keep up with the “always available” demands of the business. Data growth and the nearly ubiquitous adoption of server virtualization among mid-market and enterprise organizations are increasing the cost and complexity of storage and data availability needs. This report documents ESG Lab testing of Dell EMC Storage SC Series with a focus on the value of enhanced Live Volume support that provides always-available access with great ease of use and economics.
Published By: Equinix
Published Date: May 28, 2015
This infographic provides information on how Performance Hub is designed to improve the performance of your entire network while simplifying your infrastructure and lowering your Total Cost of Ownership.
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.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI