hadoop

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By: NetApp     Published Date: Dec 12, 2013
Learn why NetApp Open Solution for Hadoop is better than clusters built on commodity storage. This ESG lab report details the reasons why NetApp's use of direct attached storage for Hadoop improves performance, scalability and availability compared to typical internal hard drive Hadoop deployments.
Tags : netapp, lab validation, distibuted content repositor, big content scalability, enterprise reliability, intelligent object management
     NetApp
By: NetApp     Published Date: May 29, 2018
Read the IDC research report Shared Storage Offers Lower TCO than Direct-Attached Storage for Hadoop and NoSQL Deployments and learn how to: Unify insights across various data sources and multiple cloud deployments Reduce compute, capacity and operational costs Increase security and prevent data loss Plus, learn about the NetApp in-place analytics solution for your existing NAS data and how it can reduce infrastructure costs
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     NetApp
By: Pentaho     Published Date: Jan 16, 2015
Download if you need to make decisions about the architecture of the systems you work on. Sponsored by Pentaho.
Tags : embedded, big data, nosql, hadoop, customer analytics, data integration, data delivery, data management, data center
     Pentaho
By: Pentaho     Published Date: Jan 16, 2015
This ebook is recommended for IT managers, developers, data analysts, system architects, and similar technical workers, who are faced with having to replace current systems and skills with the new set required by NoSQL and Hadoop, or those who want to deepen their understanding of complementary technologies and databases. Sponsored by Pentaho.
Tags : big data, hadoop, data delivery, data management, data center
     Pentaho
By: Pentaho     Published Date: Jan 16, 2015
If you’re considering a big data project, this whitepaper provides an overview of current common use cases for big data, from entry-level to more complex. You’ll get an in-depth look at some of the most common, including data warehouse optimization, streamlined data refinery, monetizing your data, and getting a 360 degree view of your customer. For each, you’ll discover why companies are investing in them, what the projects look like, and key project considerations, including tools and platforms.
Tags : big data, nosql, hadoop, data integration, data delivery, data management, data center
     Pentaho
By: Pentaho     Published Date: Feb 26, 2015
This TDWI Best Practices report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies.
Tags : big data, big data analytics, data warehousing technologies, data storage, business intelligence, data integration, enterprise applications, data management
     Pentaho
By: Pentaho     Published Date: Nov 04, 2015
This report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies based on TDWI research plus survey responses from 325 data management professionals across 13 industries. It also covers Hadoop best practices and provides an overview of tools and platforms that integrate with Hadoop.
Tags : pentaho, analytics, platforms, hadoop, big data, predictive analytics, data management, networking, it management, knowledge management, enterprise applications, data center
     Pentaho
By: Pentaho     Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
Tags : pentaho, analytics, platforms, hadoop, big data, predictive analytics, networking, it management, knowledge management, data management
     Pentaho
By: Pentaho     Published Date: Mar 08, 2016
If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals. Read the Buyer’s Guide to Big Data Integration by CITO Research to learn: • What tools are most useful for working with Big Data, Hadoop, and existing transactional databases • How to create an effective “data supply chain” • How to succeed with complex data on-boarding using automation for more reliable data ingestion • The best ways to connect, transport, and transform data for data exploration, analytics and compliance
Tags : data, buyer guide, integration, technology, platform, research, enterprise applications
     Pentaho
By: Pentaho     Published Date: Apr 28, 2016
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
Tags : pentaho, best practices, hadoop, next generation analytics, platforms, infrastructure, data, analytics in organizations, it management, wireless, enterprise applications, data management, business technology, data center
     Pentaho
By: Pentaho     Published Date: Aug 22, 2016
This white paper provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals.
Tags : big data, data integration, bi systems, hadoop
     Pentaho
By: Pentaho     Published Date: Aug 22, 2016
This white paper covers the many options available for modernizing a data warehouse.
Tags : big data, data integration, bi systems, hadoop
     Pentaho
By: Pentaho     Published Date: Aug 22, 2016
This white paper covers six guidelines product leaders should explore during their embedded analytics evaluation.
Tags : big data, data integration, bi systems, hadoop
     Pentaho
By: Pepperdata     Published Date: Jun 25, 2015
Download the white paper, A Highly-Tuned Hadoop Cluster, to learn how to add real-time optimization technology to performance tuning strategy to: • Monitor and control the actual use of (and demand for) each kind of hardware resource by task • Identify ‘holes’ in the cluster where a node could temporarily do more work • Eliminate the need to physically separate workloads to ensure performance
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     Pepperdata
By: Pepperdata     Published Date: Jun 25, 2015
Download this whitepaper to learn how real-¬time cluster optimization technology can be used in multi¬-tenancy Hadoop environments to: • Eliminate the expense of having to physically isolate workloads • Enforce service level agreements (SLAs) based on customer¬ defined policies • Enable cluster usage tracking by job, task and user to speed troubleshooting and help with chargebacks
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     Pepperdata
By: Platfora     Published Date: Aug 03, 2015
A survey of more than 395 C-level executives, sponsored by Platfora, shows that senior leaders are optimistic about the capabilities of big data, but many still struggle with big data applications.
Tags : platfora, big data, hadoop, data center, big data analytics
     Platfora
By: RedPoint Global     Published Date: Sep 22, 2014
Download this illuminating white paper about what YARN really means to the world of big data management.
Tags : redpoint, big data, data management, big data management, hadoop
     RedPoint Global
By: RedPoint Global     Published Date: Sep 22, 2014
Download this paper to learn why the power of Hadoop 2.0 lies in enabling applications to run inside Hadoop, without the constraints of MapReduce.
Tags : redpoint, mapreduce, big data, hadoop, data integration, data management, yarn
     RedPoint Global
By: SAP     Published Date: Mar 09, 2017
Learn how CIOs can set up a system infrastructure for their business to get the best out of Big Data. Explore what the SAP HANA platform can do, how it integrates with Hadoop and related technologies, and the opportunities it offers to simplify your system landscape and significantly reduce cost of ownership.
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     SAP
By: SAS     Published Date: Sep 13, 2013
Insights from a webinar in the Applying Business Analytics Webinar Series
Tags : sas, hadoop, big data, software framework, webinar, business analytics webinar series, software development, it management
     SAS
By: SAS     Published Date: Apr 16, 2015
SAS Institute is gearing up to make a self-service data preparation play with its new Data Loader for Hadoop offering. Designed for profiling, cleansing, transforming and preparing data to load it into the open source data processing framework for analysis, Data Loader for Hadoop is a lynchpin in SAS's data management strategy for 2015. This strategy centers on three key themes: 'big data' management and governance involving Hadoop, the streamlining of access to information, and the use of its federation and integration offerings to enable the right data to be available, at the right time.
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     SAS
By: SAS     Published Date: Apr 20, 2015
This conclusions paper introduces key machine learning concepts and describes new SAS solutions – SAS In-Memory Statistics for Hadoop and SAS Visual Statistics – that enable machine learning at scale.
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     SAS
By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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     SAS
By: SAS     Published Date: May 04, 2017
Should you modernize with Hadoop? If your goal is to catch, process and analyze more data at dramatically lower costs, the answer is yes. In this e-book, we interview two Hadoop early adopters and two Hadoop implementers to learn how businesses are managing their big data and how analytics projects are evolving with Hadoop. We also provide tips for big data management and share survey results to give a broader picture of Hadoop users. We hope this e-book gives you the information you need to understand the trends, benefits and best practices for Hadoop.
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     SAS
By: SAS     Published Date: Oct 18, 2017
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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     SAS
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