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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: 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: AWS     Published Date: Sep 04, 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.
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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: SAS     Published Date: Aug 28, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
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SAS
Published By: Anaplan     Published Date: Aug 28, 2018
The findings of FSN’s Innovation in the Finance Function global survey prove insightful and compelling—and highlight the critical role that innovative planning technology plays in the field of finance. Organizations today are evolving their operating models in conjunction with rising globalization, the value of big data, technological advances, regulatory changes, and demographic shifts. They are also in pursuit of business growth with the same or—in some cases—fewer resources than before.
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innovation, finance, function, global, study
    
Anaplan
Published By: Datastax     Published Date: Aug 27, 2018
The public sector has invested in big time in big data. But there’s one thing most public sector entities are dropping the ball on: real-time data, and how it can be combined with big data to increase citizen safety and make mission-critical digital transformation projects happen on-time and on budget. Read this white paper to learn why public sector entities need both big data and real-time data if they are going to deliver on their digital transformation promises.
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Datastax
Published By: Splunk     Published Date: Aug 22, 2018
The world of artificial intelligence (AI) has finally arrived at the doorstep of IT operations. As organizations move toward using both big data and machine learning functionality to support a variety of IT operations, processes and tasks, artificial intelligence for IT operations (AIOps) platforms have emerged as a one-stop solution. Gartner has released a “Market Guide for AIOps Platforms” that provides findings about the AIOps industry and its latest developments. Download your complimentary copy to: *Gain insight into the developing industry of AIOps and its future *Learn how to optimize IT operations powered by machine learning to drive business value *Compare different AIOps platform vendors and their range of capabilities
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it event management, it event management tool, event logs, aiops platform, what is aiops, aiops vendor, market guide for aiops platforms, guide for aiops platforms, monitor end to end, itoa, aiops, predictive analysis, machine learning, event correlation, event management, it operations analytics, it analytics, ibm watson, hp monitoring, hp operations manager
    
Splunk
Published By: Splunk     Published Date: Aug 21, 2018
SIEM (security information and event management) software offers a lot of promise, but legacy SIEMs simply can't keep up with the rate and sophistication of today's cyberattacks. Organizations today require access to analytics-driven SIEMs that combine a big data platform that is optimized for machine data with advanced analytics, threat detection, monitoring tools, incident response tools and multiple forms of threat intelligence. Download your complimentary copy of “The Six Essential Capabilities of an Analytics-Driven SIEM” and learn how to dramatically improve your security posture, advanced threat detection and incident response.
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Splunk
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: TIBCO Software APAC     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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TIBCO Software APAC
Published By: Hitachi Vantara     Published Date: Aug 14, 2018
In this inforgraphic, we are going to show you the possibilities with modernised, optimized data infrastructure. In doing so, we aim to bring you ideas about how you can bring your own data-driven business vision to life, focusing on the outcomes that matter most to you. In essence, we will be discussing five main industries and the possibilities for modernized data infrastructure. We will be displaying how Banking and Financial Services, Telecommunications, manufacturing, healthcare and retail can use data, analytics and automation to optimize their business, therefore bringing about a competitive edge which previous appeared unimaginable. Using data correctly, most importantly building proper data infrastructures, can be the driving force behind obtaining a competitive edge. Data is your story. And it’s our story, too.
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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: TIBCO Software APAC     Published Date: Aug 13, 2018
The popularity of integration platform as a service (iPaaS) started with business users looking to gain control and share data among their proliferating SaaS apps?without needing IT intervention. iPaaS was then adopted by IT to support business users to ensure security measures were being maintained and to provide more of a self-service environment. Now, iPaaS has evolved from a niche solution to taking a much bigger role: Read this whitepaper to learn about: Drivers for cloud integration Five emerging uses cases for iPaaS that enable better responsiveness, APIs, event-driven capabilities, human workflows, and data analysis Questions to ask when evaluating your current solution
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TIBCO Software APAC
Published By: TIBCO Software APAC     Published Date: Aug 13, 2018
Big data has raised the bar for data virtualization products. To keep pace, TIBCO® Data Virtualization added a massively parallel processing engine that supports big-data scale workloads. Read this whitepaper to learn how it works.
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TIBCO Software APAC
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: IBM     Published Date: Aug 08, 2018
An IBM Cloud configuration completed a big data analytics workload in less time and with greater throughput than an AWS solution
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IBM
Published By: Workday     Published Date: Aug 07, 2018
Today, big data is everywhere. But only companies that know how to realize its true potential are gaining the competitive edge. Join HBR and Eric Siegel, author of Predictive Analytics: Who Will Click, Buy, Lie, or Die, to learn how you can transform data into insight, predict the future, and win.
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Workday
Published By: Datastax     Published Date: Aug 07, 2018
The public sector has invested in big time in big data. But there’s one thing most public sector entities are dropping the ball on: real-time data, and how it can be combined with big data to increase citizen safety and make mission-critical digital transformation projects happen on-time and on budget. Read this white paper to learn why public sector entities need both big data and real-time data if they are going to deliver on their digital transformation promises.
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Datastax
Published By: Dell Server     Published Date: Aug 07, 2018
IT is in the midst of one of its major transformations. IDC has characterized this paradigm shift as the “third platform,” driven by innovations in cloud, big data, mobility and social technologies. Progressive enterprises are seeking to leverage third-platform technologies to create new business opportunities and competitive differentiation through new products such as Dell EMC PowerEdge Servers powered by Intel® Xeon® Platinum processor and services, new business models and new ways of engaging customers.
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Dell Server
Published By: Workday France     Published Date: Aug 03, 2018
Comme pour tout changement, la route sera longue mais c’est une opportunité à saisir pour les RH. À long terme, si les systèmes et processus RH sont mis en place correctement, les équipes RH pourraient se retrouver sur le devant de la scène, fortes de l’efficacité de leurs  données dans le monde moderne des affaires  de plus en plus nourri par les données. Par conséquent, le RGPD peut permettre aux  RH de jouer un rôle de premier plan dans la révolution du Big Data, offrant des avantages  substantiels pour les performances  organisationnelles. 
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Workday France
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.
Tags : 
data infrastructure, big data, internet of things
    
Hitachi Vantara
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