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By: TIBCO     Published Date: Nov 09, 2015
As one of the most exciting and widely adopted open-source projects, Apache Spark in-memory clusters are driving new opportunities for application development as well as increased intake of IT infrastructure. Apache Spark is now the most active Apache project, with more than 600 contributions being made in the last 12 months by more than 200 organizations. A new survey conducted by Databricks—of 1,417 IT professionals working with Apache Spark finds that high-performance analytics applications that can work with big data are driving a large proportion of that demand. Apache Spark is now being used to aggregate multiple types of data in-memory versus only pulling data from Hadoop. For solution providers, the Apache Spark technology stack is a significant player because it’s one of the core technologies used to modernize data warehouses, a huge segment of the IT industry that accounts for multiple billions in revenue. Spark holds much promise for the future—with data lakes—a storage repo
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     TIBCO
By: IBM     Published Date: Feb 13, 2015
IBM® has created a proprietary implementation of the open-source Hadoop MapReduce run-time that leverages the IBM Platform™ Symphony distributed computing middleware while maintaining application-level compatibility with Apache Hadoop.
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     IBM
By: Dell and Intel®     Published Date: Aug 24, 2015
To extract value from an ever-growing onslaught of data, your organization needs next-generation data management, integration, storage and processing systems that allow you to collect, manage, store and analyze data quickly, efficiently and cost-effectively. That’s the case with Dell| Cloudera® Apache™ Hadoop® solutions for big data. These solutions provide end-to-end scalable infrastructure, leveraging open source technologies, to allow you to simultaneously store and process large datasets in a distributed environment for data mining and analysis, on both structured and unstructured data, and to do it all in an affordable manner.
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     Dell and Intel®
By: Solix     Published Date: Aug 03, 2015
Every CIO want to know if their infrastructure will handle it when data growth reaches 40 zettabytes by 2020. When data sets become to large, application performance slows and infrastructure struggles to keep up. Data growth drives increases cost and complexity everywhere, including power consumption, data center space, performance and availability. To find out more download the Gartner study now.
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     Solix
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
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.
Tags : e-commerce business, red hat, satellite
     Red Hat
By: Group M_IBM Q1'18     Published Date: Jan 04, 2018
IBM® InfoSphere® Big Match for Hadoop helps you analyze massive volumes of structured and unstructured customer data to gain deeper customer insights. It can enable fast, efficient linking of data from multiple sources to provide complete and accurate customer information—without the risks of moving data from source to source. The solution supports platforms running Apache Hadoop such as IBM Open Platform, IBM BigInsights, Hortonworks and Cloudera.
Tags : hadoop, infosphere, data, customer insights
     Group M_IBM Q1'18
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.
Tags : data, storage, scalability, cost efficiencies, pure storage
     Pure Storage
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.
Tags : teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges
     Teradata
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.
Tags : teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges
     Teradata
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
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.
Tags : ibm, data, big data, integration, hadoop
     IBM
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.
Tags : ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science
     IBM
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.
Tags : ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management
     IBM
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
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.
Tags : data integration, data security, data optimization, data virtualization, database security, data migration, data assets, data delivery
     IBM
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.
Tags : data management, yellow elephant, business benefits, information management
     IBM
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.
Tags : machine learning, apache spark, additional resources, big data, ibm
     IBM APAC
By: IBM     Published Date: Jul 05, 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.
Tags : ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management
     IBM
By: IBM     Published Date: Oct 27, 2016
IBM Analytics for Apache Spark for Bluemix is an open-source cluster computing framework with in-memory processing to speed analytic applications up to 100 times faster compared to other technologies on the market today. Optimized for extremely fast and large scale data processing-you can easily perform big data analysis from one application.
Tags : ibm, apache spark, bluemix, analytics, data science
     IBM
By: IBM     Published Date: Nov 30, 2016
Learn how to create powerful analytic apps with IBM Cloudant, dashDB and Apache Spark. This presentation will contain demos of real-life use cases e.g. machine learning predictive analytics, Graph-parallel computation and more.
Tags : ibm, cloud, analytic apps, apps, cloudant
     IBM
By: IBM     Published Date: Nov 30, 2016
You’ve taken the first step and already know that a document- oriented database is the right database for your application. From here, you still have to decide where and how you’ll deploy the software and its associated infrastructure. These decisions lead to additional considerations around administrative overhead, technical support, open-source options, data sovereignty and security, and more. This paper aims to outline the deployment options available when you select IBM® Cloudant® as your JSON store.
Tags : ibm, cloud, cloudant managed service, cloudant local, apache couchdb
     IBM
By: IBM     Published Date: Jan 18, 2017
Data matters more than ever to business success. But value does not come from data alone. Rather, it comes from the insights enabled by data. No matter what your role is, or where you are in your data journey, you are looking for ways to drive innovation.
Tags : ibm, analytics, aps data, open data science, data science, apache spark
     IBM
By: IBM     Published Date: Jan 18, 2017
You’ve taken the first step and already know that a document- oriented database is the right database for your application. From here, you still have to decide where and how you’ll deploy the software and its associated infrastructure. These decisions lead to additional considerations around administrative overhead, technical support, open-source options, data sovereignty and security, and more. This paper aims to outline the deployment options available when you select IBM® Cloudant® as your JSON store.
Tags : ibm, cloud, analytics, cloudant managed service, cloudant local, apache couchdb, databases, data science
     IBM
By: Datastax     Published Date: Apr 04, 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.
Tags : datastax, netflix, mint bills, digital, personalization
     Datastax
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