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Published By: Datastax     Published Date: Sep 27, 2019
In this white paper, you’ll learn about the three most important issues surrounding data management in the age of IoT. You'll also learn how DataStax Enterprise, built on Apache Cassandra™ and native to hybrid cloud environments, is paving the way for the future of data management. Download the white paper to learn more now.
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Datastax
Published By: Datastax     Published Date: Oct 11, 2019
The first and most important step to building a successful, scalable application is getting the data model right. In this white paper, you’ll get a detailed, straightforward, five-step approach to creating the right data model right out of the gate—from mapping workflows, to practicing query-first design thinking, to using Cassandra data types effectively.
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Datastax
Published By: DataStax     Published Date: Mar 10, 2017
Netflix, Intuit and Clear Capital. These 3 innovative companies have one thing in common. They are altering their business landscape and transforming the way people live and work through highly personalized applications. And they're doing this with Apache Cassandra™ and DataStax. Download this white paper and learn why relational technologies failed to meet the demands of Netflix, Mint Bills and Clear Capital and how these enterprises modernize their Web and Mobile applications with DataStax to drive customer engagement, loyalty and lifetime value.
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DataStax
Published By: Red Hat     Published Date: Jan 01, 2009
The number 1 independent tire retailer tried, unsuccessfully, to build an online platform based on Windows. Ultimately, the company succeeded instead with a solution built with Red Hat Enterprise Linux, Red Hat Network Satellite, Apache, WebLogic, and IBM Lotus Domino Server.
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e-commerce business, red hat, satellite
    
Red Hat
Published By: Pure Storage     Published Date: Jul 03, 2019
University of California-Berkeley researchers employ the latest tools built on Apache Spark to accelerate DNA sequencing in pursuit of precision medicine. Adding Pure FlashBlade™ from Pure Storage® has significantly reduced the time needed to sequence data-intensive DNA samples and analyze results.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Apache® Spark™ has become one of the most rapidly adopted open source platforms in history. Demand is predicted to grow at a compound annual rate of 67% per year between 2017 and 2020, with the cumulative Spark market valued at more than $9 billion during that period, according to the research firm MarketAnalysis.com.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 10, 2019
Pure Storage, a pioneer in block-based flash arrays, has developed a technology called FlashBlade, designed specifically for file and object storage environments. With FlashBlade, IT teams now have a simple-to-manage shared storage solution that delivers the performance and capacity needed to bring Spark deployments on premise. To help gain a deeper understanding of the storage challenges related to Spark and how FlashBlade addresses them, Brian Gold of Pure Storage sat down with veteran technology journalist Al Perlman of TechTarget for a far-reaching discussion on Spark trends and opportunities.
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Pure Storage
Published By: Pure Storage     Published Date: Jan 12, 2018
Apache Spark has become a critical tool for all types of businesses across all industries. It is enabling organizations to leverage the power of analytics to drive innovation and create new business models. The availability of public cloud services, particularly Amazon Web Services, has been an important factor in fueling the growth of Spark. However, IT organizations and Spark users are beginning to run up against limitations in relying on the public cloud—namely control, cost and performance.
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data, storage, scalability, cost efficiencies, pure storage
    
Pure Storage
Published By: Teradata     Published Date: Jan 30, 2015
This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges, technology, aster, snap, framework, apache, hadoop, yarn, architectures, data management, cloud computing
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges, technology, framework, apache, hadoop, architecture, warehouse, optimization, security, scalability, consistency, flexibility, data management
    
Teradata
Published By: Lucidworks     Published Date: Dec 14, 2016
You feel you’ve got a pretty good handle on the following challenges—exponentially increasing amounts of data, ever-increasing user expectations, and limited IT resources—along with your technical requirements. That’s why you chose to build your search app with Apache Solr. Download now to learn more about Apache SoIr and Lucidworks Fusion.
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Lucidworks
Published By: TIBCO Software     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
Published By: Splunk     Published Date: Aug 17, 2018
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs. Download “The Essential Guide to Infrastructure Machine Data” for: The benefits of machine data for network, remote, web, cloud and server monitoring IT infrastructure monitoring data sources to include in your machine data platform Machine data best practices
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Attunity     Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
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data streaming, kafka, metadata integration, metadata, data streaming, apache kafka, data integration, data analytics, database transactions, streaming environments, real-time data replication, data configuration
    
Attunity
Published By: Red Hat APAC     Published Date: Jun 27, 2019
Red Hat® JBoss® Fuse is a lightweight integration platform that reduces the pain of connecting applications, services, processes, and devices for comprehensive and efficient solutions. JBoss Fuse includes the popular and versatile Apache Camel project, an implementation of the most commonlyused enterprise integration patterns. With integration patterns and more than 150 connectors ready to use, JBoss Fuse supports integration across the extended enterprise—including applications and services on-premise, on mobile devices, or in the cloud. JBoss Fuse is complemented by Red Hat JBoss Developer Studio for easier development of integration solutions and Red Hat JBoss Operations Networkfor monitoring of deployed solutions.
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Red Hat APAC
Published By: Splunk     Published Date: Sep 10, 2018
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential. Download this white paper to learn: *How organizations can answer critical questions that have been impeding business success *How the financial services industry can make great strides in security, compliance and IT *Common machine data sources in financial services firms
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Splunk     Published Date: Sep 10, 2018
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers. For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy. Download this report to learn: How to reduce monitoring blind spots when creating an ITOM strategy How to address ITOM requirements across IT and non-IT groups Distinct layers across ITOM Potential functionality gaps with domain-specific products
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Pure Storage     Published Date: Oct 09, 2018
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude
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Pure Storage
Published By: IBM     Published Date: Feb 22, 2016
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.
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ibm, data, big data, integration, hadoop, enterprise applications, data management
    
IBM
Published By: IBM     Published Date: Jul 14, 2016
This video describes how data scientists, analysts and business users can save precious time by using a combination of SPSS and Spark to uncover and act on insights in big data.
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ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science, software development, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.
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ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management, enterprise applications, data management
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.
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IBM
Published By: IBM     Published Date: Apr 18, 2017
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data. To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
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data integration, data security, data optimization, data virtualization, database security, data migration, data assets, data delivery
    
IBM
Published By: IBM     Published Date: Jul 06, 2017
Known by its iconic yellow elephant, Apache Hadoop is purpose-built to help companies manage and extract insight from complex and diverse data environments. The scalability and flexibility of Hadoop might be appealing to the typical CIO but Aberdeen's research shows a variety of enticing business-friendly benefits.
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data management, yellow elephant, business benefits, information management
    
IBM
Published By: IBM APAC     Published Date: Aug 25, 2017
Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science, but the barrier to entry has been high, not only in cost, but also in the need for specialized talent.
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machine learning, apache spark, additional resources, big data, ibm
    
IBM APAC
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