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By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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     Group M_IBM Q418
By: Group M_IBM Q3'19     Published Date: Jun 27, 2019
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data.
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     Group M_IBM Q3'19
By: MarkLogic     Published Date: Nov 07, 2017
Today, data is big, fast, varied and constantly changing. As a result, organizations are managing hundreds of systems and petabytes of data. However, many organizations are unable to get the most value from their data because they’re using RDBMS to solve problems they weren’t designed to fix. Why change? In this white paper, we dive into the details of why relational databases are ill-suited to handle the massive volumes of disparate, varied, and changing data that organizations have in their data centers. It is for this reason that leading organizations are going beyond relational to embrace new kinds of databases. And when they do, the results can be dramatic.
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     MarkLogic
By: Oracle     Published Date: Jun 04, 2019
This survey report shows that while finance departments take responsibility for ensuring there are data-management strategies in place that include securing their organisation’s data, they struggle to manage the huge volumes of data and gain valuable insights from it. This affects both their confidence in the security of the data and their ability to manage it ethically. The number one priority of finance leaders is to enforce technologies that enable insights instantly, any place, anytime, to enable better forecasting. Read report
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     Oracle
By: McAfee     Published Date: Nov 07, 2014
Segundo o relatório “Agulha em um palheiro de dados” (Needle in a Datastack), as empresas estão vulneráveis a violações de segurança porque não são capazes de analisar ou armazenar adequadamente o Big Data. Esses volumes cada vez maiores de eventos, bem como de dados sobre ativos, ameaças, usuários e outros dados relevantes, criaram um grande desafio para as equipes de segurança em relação ao Big Data. Para solucionar esse desafio, as empresas abandonaram as arquiteturas tradicionais de gerenciamento de dados para adotar sistemas dedicados ao gerenciamento de dados de segurança na era das APTs (ameaças persistentes avançadas).
Tags : siem, big security data, segurança do big data, informações de segurança, ameaças avançadas, ameaças persistentes avançadas, apt, inteligência de segurança
     McAfee
By: MarkLogic     Published Date: Mar 29, 2018
Real World Evidence (RWE) requires the correlation of complex, frequently changing, unstructured data. To the enterprise architect, that means extracting value from data that doesn't neatly fit solutions. In this white paper, we dive into the details of why relational databases are ill-suited to handle the massive volumes of disparate, varied, and changing data that is required to be successful with RWE. It is for this reason that leading life science organizations are going beyond relational to embrace new kinds of databases. And when they do, the results can be dramatic.
Tags : data, integration, volume, optimization, architect, enterprise
     MarkLogic
By: HP Inc.     Published Date: Jun 20, 2019
Four billion people now generate four quintillion bytes of data every day - and with the number of IoT devices set to increase to three times the global population by 2022 - volumes will only continue to rise. The challenge is processing the data. This is why machine learning, deep learning and all the other developing forms of AI must deliver the analytics toolset businesses need to compete.
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     HP Inc.
By: Oracle     Published Date: Jun 10, 2019
Data is central to marketing function, yet research shows that 50% of CMOs struggle to manage data volumes, or gain insights. Read how to address data ethics and management in your organisation. Marketing’s top priority is to enforce technologies enabling real time data insights to drive instant, personalised CX. Meanwhile, only 30% of CMOs say they have data visualisation dashboards to analyse specific sets of data. Read the findings
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     Oracle
By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
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     Oracle
By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
Tags : 
     Oracle
By: Oracle     Published Date: Oct 20, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by the aggressive build-out for cloud computing. Big data and machine learning applications that perform tasks such as fraud and intrusion detection, trend detection, and click-stream and social media analysis all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of business up, and organizations need to support their customers with real-time data. The task of managing sensitive information while capturing, analyzing, and acting upon massive volumes of data every hour of every day has become critical. These challenges have dramatically changed the way that IT systems are architected, provisioned, and run compared to the past few decades. Most compani
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     Oracle
By: Gemini Data     Published Date: Jan 16, 2018
The increasing reliance on big data platforms for all functions of the organization has been transformative. As these environments mature and data volumes increase, organizations face infrastructure and management scalability challenges. Gemini Enterprise Manager simplifies deployment and management with a turnkey, NoOps appliance, providing simplicity, security, and speed to accelerate the time to value for any analysis use case. Manager allows you to control your Splunk deployment as a single, unified solution deployed on premises, in the cloud or both.
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     Gemini Data
By: QlikTech     Published Date: Aug 27, 2013
There is an incredible amount of interest in the topic of Big Data at present: for some organisations its use is an operational reality, providing unprecedented ability to store and analyse large volumes of disparate data that are critical to the organization’s competitive success. It has enabled people to identify new opportunities and solve problems they haven’t been able to solve before. Download this whitepaper to learn more.
Tags : big data, qlikview, big data solutions, opportunity, business opportunity, competitive success, store, analyse
     QlikTech
By: Dell     Published Date: Sep 12, 2013
Information is the engine of business growth in the digital age. Market intelligence, customer information, intellectual property and other data can be harnessed to create a quantifiable competitive advantage for a company and lay the path for future expansion. Yet as critical as data can be, the sweeping year-over-year proliferation in data volumes can quickly overwhelm an IT organization. This is much more than a budget problem: Without an effective data storage strategy, an organization is putting a critical resource at risk.
Tags : data storage management, microsoft windows server, market intelligence, customer information, intellectual property, data, it organizations, storage strategy
     Dell
By: AWS     Published Date: Nov 02, 2017
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it’s readily available to be categorized, processed, analyzed, and consumed by diverse groups within an organization. Since data - structured and unstructured - can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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     AWS
By: Cloudian     Published Date: Feb 20, 2018
We are critically aware of the growth in stored data volumes putting pressure on IT budgets and services delivery. Burgeoning volumes of unstructured data commonly drive this ongoing trend. However, growth in database data can be expected as well as enterprises capture and analyze data from the myriad of wireless devices that are now being connected to the Internet. As a result, stored data growth will accelerate.
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     Cloudian
By: Talend     Published Date: Nov 02, 2018
Siloed data sources, duplicate entries, data breach risk—how can you scale data quality for ingestion and transformation at big data volumes? Data and analytics capabilities are firmly at the top of CEOs’ investment priorities. Whether you need to make the case for data quality to your c-level or you are responsible for implementing it, the Definitive Guide to Data Quality can help. Download the Definitive Guide to learn how to: Stop bad data before it enters your system Create systems and workflow to manage clean data ingestion and transformation at scale Make the case for the right data quality tools for business insight
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     Talend
By: SRC,LLC     Published Date: Jun 01, 2009
Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
Tags : src, data transformation, classification, business value, geographic business intelligence, geo-bi, etl, extract
     SRC,LLC
By: AWS - ROI DNA     Published Date: Aug 09, 2018
In today's big data digital world, your organization produces large volumes of data with great velocity. Generating value from this data and guiding decision making require quick capture, analysis and action. Without strategies to turn data into insights, the data loses its value and insights become irrelevant. Real-time data inegration and analytics tools play a crucial role in harnessing your data so you can enable business and IT stakeholders to make evidence-based decisions
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     AWS - ROI DNA
By: Vertica     Published Date: Mar 15, 2010
In a world of growing data volumes and shrinking IT budgets, it is critical to think differently about the efficiency of your database and storage infrastructure. The Vertica Analytic Database is a high-performance, scalable and cost-effective solution that can bring dramatic savings in hardware, storage and operational costs.
Tags : vertica, ec2, cdr, elastic, saas, cdr, cloud computing, data management
     Vertica
By: IBM     Published Date: Sep 10, 2009
With ever-increasing data growth worldwide, organizations must find smarter ways to store and manage their massive volumes of data. Learn how IBM® Tivoli® Storage Management software solutions help to maximize your current storage environment and reduce operational and capital costs while improving service and managing risks.
Tags : storage management, ibm, ibmtivoli, fastback, san, data growth, recovery management, reducing costs
     IBM
By: TeamQuest     Published Date: Apr 09, 2014
TeamQuest Director of Global Services Per Bauer explains how to manage services in relation to servers, storage, network, power and floor space. Understand costing data, incidents, business transaction volumes, and demand forecasts. Watch this short video to learn more about Optimization in a Box and how to quickly improve your ability to optimize business services today.
Tags : teamquest, virtualization, it professionals, optimize software, distributed environment, data center, performance
     TeamQuest
By: CDW     Published Date: Aug 04, 2016
As data volumes grow, you need more than just storage space. Let us help you orchestrate a solution that brings you the scalability and agility you need to move your organization forward. Storage needs are changing rapidly, and legacy appliances and processes just can’t keep up. Old systems are running slowly and filling up fast. At CDW, we can help you evolve your storage with a smart solution that’s ready for what lies ahead.
Tags : data, technology, storage, best practices, best solutions
     CDW
By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? Since data—structured and unstructured—can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
Tags : data, lake, amazon, web, services, aws
     AWS
By: AWS     Published Date: Nov 28, 2018
Financial institutions run on data: collecting it, analyzing it, delivering meaningful insights, and taking action in real time. As data volumes increase, organizations demand a scalable analytics platform that can meet the needs of data scientists and business users alike. However, managing an on-premises analytics environment for a large and diverse user base can become time-consuming, costly, and unwieldy. Tableau Server on Amazon Web Services (AWS) is helping major Financial Services organizations shift data visualization and analytics workloads to the cloud. The result is fewer hours spent on manual work and more time to ask deeper questions and launch new data analyses, with easily-scalable support for large numbers of users. In this webinar, you’ll hear how one major asset management company made the shift to cloud data visualization with Tableau Server on AWS. Discover lessons learned, best practices tailored to Financial Services organizations, and starting tactics for scalable analytics on the cloud.
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     AWS
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