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Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache
    
Altiscale
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
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Amazon Web Services
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: 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
Published By: BMC ASEAN     Published Date: Dec 18, 2018
Digital transformation encompasses both technological and human components. While many initiatives focus on ensuring that a company’s multi-cloud infrastructure is agile enough to meet changing demands around cloud mobile, Internet of Things (IoT), and big data, it’s equally important to empower business workers with the modern digital tools they need to be successful today. Artificial intelligence and machine learning can play a vital role on both of these fronts. In fact, 78 percent of CIOs and senior IT leaders are already looking to AI to address complexity,1 and by 2019, 30 percent of IT service desks will utilize machine learning to free up support capacity.2 The magnitude of change has forced companies to take stock of the experience they offer employees. As digital natives3 enter and advance in the workforce, talent retention is now a top priority. These workers expect to have the best tools; 93 percent of millennials cited modern and up-to-date technology as one of the most
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BMC ASEAN
Published By: Carbonite     Published Date: Oct 05, 2017
Not long ago, the biggest concern for IT decision makers considering moving workloads to the cloud was security. That’s no longer the case. Today, the main obstacle to cloud adoption is different but familiar: the pain of migrating data. Traditional data migration causes major headaches: • Hours or days of downtime while servers replicate and stakeholders verify functionality • Data loss and migration failures due to inadequate tools and inability to test • Key internal resources diverted from strategic initiatives
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data migration, cloud security, carbonite, data security, cloud platform, cloud adoption
    
Carbonite
Published By: Cisco     Published Date: Jun 21, 2016
Enterprises are leveraging advancements in what IDC calls “3rd Platform” technologies — cloud, Big Data, mobility, and social — to create new business opportunities and gain competitive advantage. These trends are putting more pressure on IT organizations to transform their datacenter operations to better support business initiatives aimed at reducing costs, increasing revenue, and strengthening customer relationships. This has driven increased demand for converged or integrated systems that provide the tools needed to reduce capital costs, improve operational efficiencies and, ultimately, increase agility within the datacenter.
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Cisco
Published By: Cisco     Published Date: Jun 21, 2016
Enterprises are leveraging advancements in what IDC calls “3rd Platform” technologies — cloud, Big Data, mobility, and social — to create new business opportunities and gain competitive advantage. These trends are putting more pressure on IT organizations to transform their datacenter operations to better support business initiatives aimed at reducing costs, increasing revenue, and strengthening customer relationships. This has driven increased demand for converged or integrated systems that provide the tools needed to reduce capital costs, improve operational efficiencies and, ultimately, increase agility within the datacenter.
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Cisco
Published By: Datawatch     Published Date: Mar 21, 2014
Big Data is not a new problem. Companies have always stored large amounts of data—structured like databases, unstructured like documents—in multiple repositories across the enterprise. The most important aspect of big data is not how big it is, or where it should be stored, or how it should be accessed. It’s the efficacy of business intelligence tools to plumb its depths for patterns and trends, to derive insight from it that will give companies competitive advantage in an increasingly challenging business climate. Visualization allows companies to analyze big data in real-time across a variety of sources in order to make better business decisions.
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visual data discovery, decision making software, data variety, business analysis, data visualization, big data, business analytics, business intelligence, real-time data, real-time data visualization, real-time data discovery, data variety software, data discovery software, data analysis software, data mining tools, data extraction, data reporting, pdf to excel, business reporting, data solutions
    
Datawatch
Published By: EMC Corporation     Published Date: Jul 07, 2013
Businesses that exploit Big Data to improve strategy and execution are distancing themselves from competitors. The Big Data solution from EMC provides market-leading, scale-outing storage, a unified analytics platform, and business process and application development tools. Together, these allow organizations to draw deeper insights and become a more predictive organization.
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emc, big data, strategy improvement, market leader, scale-outing storage, analytics platform, technology, application development
    
EMC Corporation
Published By: EMC Corporation     Published Date: Jul 07, 2013
While the concept of big data is nothing new, the tools and technology and now in place for companies of all types and sizes to take full advantage. Enterprises in industries such as media, entertainment, and research and development have long been dealing with data in large volumes and unstructured formats - data that changes in near real time. However, extracting meaning from this data has been prohibitive, often requiring custom-built, expensive technology. Now, thanks to advancements in storage and analytics, all organizations can leverage big data to gain the insight needed to make their businesses more agile, innovative, and competitive.
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big data, emc, technology, storage, analytics, data management
    
EMC Corporation
Published By: FICO     Published Date: Nov 10, 2015
"ACG Michigan, a large auto insurance underwriter in the US state of Michigan, needed a user-friendly system that would enable its agents (internal and independent) to churn out precise and consistent policy quotes and underwriting decisions. They turned to FICO Blaze Advisor decision rules management system to create an enterprise decision management framework to execute decisions. Learn more on how FICO Blaze Advisor helped ACG Michigan automate its underwriting About FICO FICO (NYSE: FICO), formerly known as Fair Isaac, is a leading analytics software company, helping businesses in 90+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company's groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimiz
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decision analytics, optimization, profitability
    
FICO
Published By: FICO     Published Date: Jun 07, 2016
With the increasing mainstream adoption of big data infrastructure — highly distributed file storage and query tools — more businesses are taking a new look at what business intelligence and analytics can do to grow revenue, increase profits and ultimately develop strategic relationships with customers.
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FICO
Published By: FICO     Published Date: Dec 04, 2017
In an era where Big Data decisions demand high-powered tools, organizations everywhere are still mired in complex spreadsheets that limit the speed and precision of their critical customer interactions. Read this fact sheet to learn how you can evolve beyond what spreadsheets alone can achieve: • Allow business users to easily create and compare “what if” scenarios, interact with compelling visualizations, and challenge, improve and build trust with stakeholders and collaborators • Rapidly deploy new optimization features and applications practically at the speed of thought – without leaning on IT – while leveraging existing investments in other analytic tools (such as R, SAS, MATLAB, and even Excel) Download Now
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fico, optimization, modeler, industry, analytic, tool, business, solutions
    
FICO
Published By: Hitachi Vantara     Published Date: Aug 02, 2018
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT), big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
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data infrastructure, big data, internet of things
    
Hitachi Vantara
Published By: Hitachi Vantara     Published Date: Aug 14, 2018
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT), big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
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data infrastructure, big data, internet of things
    
Hitachi Vantara
Published By: HP     Published Date: Jan 20, 2015
There are many new ways Big Data analytics can significantly boost marketing and promotional efforts through real-time and historical analysis of online data, such as clickstream or purchase transactions. Unstructured data based on social media—even photos and video—offers enormous potential when analyzed with the right tools.
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big data, hp haven, scalable, secure data platform, ecosystem, security
    
HP
Published By: HPE Intel     Published Date: Mar 09, 2016
State agency and higher education CIOs are under intense pressure to modernize their legacy IT systems. Whether that requires moving from one infrastructure to another, or moving to or from the cloud, the chief challenge remains: How to migrate data and applications without disrupting critical operations, ensure improved performance and avoid pricing and other traps that often accompany virtualizing in the cloud. Learn what’s made the difference in overcoming the impediments to workload migrations from IT leaders who’ve lived through big data and application moves. Topics include: Preparing workloads for cloud on-boarding – and off-boarding Tools for simplifying the consolidation and migration of apps and supporting services Forecasting workloads, and planning for associated testing, training, and other costs
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HPE Intel
Published By: IBM     Published Date: Oct 30, 2014
Filled with exotic terms like “Hadoop” and “Data Scientist”, Big Data, business intelligence and analytics have always been the domain of the biggest enterprises with huge teams to devote to analyzing data. But thanks to the latest technology advances businesses of almost any size can utilize tools to help inform every part of business decision-making. This SlashGuide looks at a recent Slashdot Pulse research study on BI/BA and discusses what’s really important when it comes to Big Data – and what businesses can do now to capitalize on the trend.
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business analytics, big data, bi/ba
    
IBM
Published By: IBM     Published Date: Oct 31, 2014
Cloud computing, mobility, social technology, DevOps, embedded intelligence, and big data bring complexity to the datacenter. To cope, IT leaders are turning to new IT service management tools to help them analyze and optimize their on-premises, cloud, and hybrid operations while accelerating their pace of innovation.
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it delivery, smarter it, social technology, devops
    
IBM
Published By: IBM     Published Date: Apr 29, 2015
IBM InfoSphere BigInsights for Hadoop enables organizations to efficiently manage and mine large volumes of diverse data for valuable insights. IBM builds on a 100% Apache Hadoop foundation with common tools such as spreadsheets, R analytics and SQL access for greater usability.
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bigsheets, data management, business intelligence, workload optimization, data, sql
    
IBM
Published By: IBM     Published Date: Apr 14, 2017
Any organization wishing to process big data from newly identified data sources, needs to first determine the characteristics of the data and then define the requirements that need to be met to be able to ingest, profile, clean,transform and integrate this data to ready it for analysis. Having done that, it may well be the case that existing tools may not cater for the data variety, data volume and data velocity that these new data sources bring. If this occurs then clearly new technology will need to be considered to meet the needs of the business going forward.
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data integration, big data, data sources, business needs, technological advancements, scaling data
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes. The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
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data integration, data security, data optimization, data virtualization, database security, data analytics, data innovation
    
IBM
Published By: IBM Watson Health     Published Date: Nov 10, 2017
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
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population health management, big data, data, data analytics, big data solution, data infrastructure, analytic tools, predictive modeling
    
IBM Watson Health
Published By: Interactive Intelligence     Published Date: Jan 17, 2014
Learn the performance “metrics that matter” for the contemporary contact center, as the definition of efficient customer service evolves.
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contact center, strategic planning, budgeting tools, customer experience, big data, budgeting tools, workforce management, data solution, planning solutions
    
Interactive Intelligence
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