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big data and analytics

Results 1 - 25 of 154Sort Results By: Published Date | Title | Company Name
Published By: Advizex     Published Date: Sep 25, 2013
The challenge of Big Data is more than a question of size; it’s about time to insight and action. With the exponential growth of unstructured data such as social media, video and the raw data generated by smartphones and other “intelligent” machines, businesses are buried under an avalanche of data that renders even best-effort analytics slow and sometimes unreliable. As many businesses are learning in this age of Big Data, it’s not just what you know, but when you know it and how much you trust it. Download this white paper and learn that with SAP HANA, companies can react intelligently at the speed of thought to capture new opportunities.
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sap hana, sap, real time analytics, raw data, generation, analytics, big data, business intelligence, trusting big data, insight, action, enterprise applications
    
Advizex
Published By: Amazon Web Services     Published Date: Oct 09, 2017
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organisations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt. However, given big data's power to transform business, it's critical that organisations overcome these challenges and realise the value of big data. The cloud can help organisations to do so. Drawing from IDG's 2015 Big Data and Analytics Survey, this white paper analyses the top five challenges companies face when undergoing a big data initiative and explains how they can effectively overcome them.
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amazon, web services, intel, migration, data warehousing, organization optimization, security, software
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Defining the Data Lake “Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
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
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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: 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: Attunity     Published Date: Nov 15, 2018
With the opportunity to leverage new analytic systems for Big Data and Cloud, companies are looking for ways to deliver live SAP data to platforms such as Hadoop, Kafka, and the Cloud in real-time. However, making live production SAP data seamlessly available wherever needed across diverse platforms and hybrid environments often proves a challenge. Download this paper to learn how Attunity Replicate’s simple, real-time data replication and ingest solution can empower your team to meet fast-changing business requirements in an agile fashion. Our universal SAP data availability solution for analytics supports decisions to improve operations, optimize customer service, and enable companies to compete more effectively.
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Attunity
Published By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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AWS
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: AWS     Published Date: Sep 05, 2018
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
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AWS
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: BlueData     Published Date: Aug 19, 2015
Big Data is on virtually every enterprise’s to-do list these days. Recognizing both its potential and competitive advantage, companies are aligning a vast array of resources to access and analyze this strategic asset. However, despite best intentions, the majority of these Big Data initiatives are either extremely slow in their implementation or are not yielding the results and benefits that enterprises expect. Download this white paper to learn how to solve the Big Data intention-deployment gap and see how you can make your infrastructure in a flexible, easy-to-use platform that will provide in-depth analytics.
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big data, big data intention-deployment, in-depth analytics, hadoop, data center
    
BlueData
Published By: CDW     Published Date: Aug 04, 2016
Changing workloads are pushing organizations to consider new infrastructure options. The latest designs address growing interest and the unique demands of the Internet of Things and Big Data. When cloud service provider Virdata needed to develop a highly scalable platform to collect information from millions of devices and offer Big Data analytics to its customers, it chose a converged infrastructure. Download this white paper to learn more!
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technology, data, converged systems, big data, cloud, productivity, internet, analytics
    
CDW
Published By: Cisco     Published Date: Jul 11, 2016
Companies rely on an expanding set of applications to compete in today's rapidly evolving business environment: - They rely on a fast-growing array of applications and devices (email, collaboration tools, and smartphones/tablets) to communicate and conduct business with customers and business partners. - They are creating, collecting, and repurposing large, unstructured data sets in life sciences, geophysics, media, and manufacturing. - They are collecting, storing, and analyzing more social and sensor-generated data about environments, products, customers, and transactions. The promise of better and faster data-driven decision making based on all this information is pushing big data and analytics (BDA) technology to the top of executive agendas. To succeed, CIOs must place a laserlike investment focus on datacenter solutions that allow them to deliver scalable, reliable, and flexible infrastructure for fast-growing BDA environments. Read more to learn how!
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Cisco
Published By: Cisco     Published Date: Sep 07, 2016
This white paper provides a detailed breakdown of big data and analytics - the Cisco way.
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Cisco
Published By: Cisco EMEA     Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
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big data, analytics, virtualization, cloudera, ibm, sas, sap, splunk
    
Cisco EMEA
Published By: Cisco EMEA     Published Date: Mar 05, 2018
The competitive advantages and value of BDA are now widely acknowledged and have led to the shifting of focus at many firms from “if and when” to “where and how.” With BDA applications requiring more from IT infrastructures and lines of business demanding higher-quality insights in less time, choosing the right infrastructure platform for Big Data applications represents a core component of maximizing value. This IDC study considered the experiences of firms using Cisco UCS as an infrastructure platform for their BDA applications. The study found that Cisco UCS contributed to the strong value the firms are achieving with their business operations through scalability, performance, time to market, and cost effectiveness. As a result, these firms directly attributed business benefits to the manner in which Cisco UCS is deployed in the infrastructure.
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big data, analytics, cisco, value, business, enterprise
    
Cisco EMEA
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: ClearSlide, Inc     Published Date: Feb 25, 2015
Organizations treat customer data as a key component of CRM. Many organizations see an effective customer data management strategy as an important cornerstone of their CRM strategy. Most organizations are moving beyond tactical CRM initiatives focused on saving cost and driving efficiency to making their organization more effective and focused on driving better customer engagement and experience. Customer data is used to enhance customer experiences, improve service quality, target marketing efforts, capture customer sentiment, increase upsell opportunities and trigger product and service innovation.
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crm, big data, customer engagement, customer service
    
ClearSlide, Inc
Published By: ClearStory     Published Date: Oct 07, 2014
When it comes to “big data,” Gartner boils it all down to “value.” In their 2014 Cool Vendors for Big Data report, ClearStory Data was among 4 companies profiled. According to Gartner, ClearStory is “for companies looking at big data business intelligence and analytics from a data variety perspective.”
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big data, value, business intelligence, analytics, data variety, data center
    
ClearStory
Published By: Dassault Systèmes     Published Date: Jul 21, 2017
Obtaining a first-mover competitive advantage or faster time-to-market requires a new wave in analytics. Dassault Systèmes remains a leading innovator in Product Lifecycle Management (PLM) and has invested heavily in analytical technologies to further drive business benefits for its customers in the related areas of planning, simulation, insight and optimization. This white paper examines the challenges peculiar to PLM and why Dassault Systèmes’ EXALEAD offers the most appropriate solution. It also clearly positions EXALEAD PLM Analytics alongside related technologies like BI, data-warehousing and Big Data solutions. Understand and implement PLM Analytics to access actionable information, support accurate decision-making, and drive performance.
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product solutions, lifecycle management, tech products, data management tools, pdm, plm, process automation, product development speed, manufactures
    
Dassault Systèmes
Published By: Dun & Bradstreet     Published Date: Mar 03, 2017
Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry The democratization of financial services data and technology, together with more intense competition, makes the needs of today’s market participants vastly different from those of previous generations. Firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments. By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models. View our research report to learn how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.
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Dun & Bradstreet
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, networking
    
EMC Corporation
Published By: EMC Corporation     Published Date: Jul 07, 2013
Forward-looking enterprises know there's more to big data than strong and managing large volumes of information. Big data presents an opportunity to leverage analytics and experiment with all available data to derive value never before possible with traditional business intelligence and data warehouse platforms. Through a modern, big data platform that facilitates self-service and collaborative analytics across all data, organizations become more agile and are able to innovate in new ways.
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enterprises, storage, information management, technology, platform, big data analytics, emc, self service, business intelligence, security, enterprise applications
    
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, security, platforms
    
EMC Corporation
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