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Published By: SAS     Published Date: Aug 28, 2018
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
Published By: Intel     Published Date: Aug 19, 2014
Around the world and across all industries, high-performance computing is being used to solve today’s most important and demanding problems. More than ever, storage solutions that deliver high sustained throughput are vital for powering HPC and Big Data workloads.
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intel, data center, big data, hadoop, cloud, lustre, hpc
    
Intel
Published By: IBM     Published Date: Mar 05, 2014
If you specialize in relational database management technology, you’ve probably heard a lot about “big data” and the open source Apache Hadoop project. Perhaps you’ve also heard about IBM’s new Big SQL technology, which enables IBM® InfoSphere® BigInsights™ users to query Hadoop data using industry-standard SQL. Curious? This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, ibm big sql, sql, database management, database management technology, software, tables, queries, data platform, big sql architecture, programming language, relational database management system, rdbms
    
IBM
Published 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.
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redpoint, big data, data management, big data management, hadoop
    
RedPoint Global
Published 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.
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redpoint, mapreduce, big data, hadoop, data integration, data management, yarn
    
RedPoint Global
Published By: WANdisco     Published Date: Oct 15, 2014
In this Gigaom Research webinar, the panel will discuss how the multi-cluster approach can be implemented in real systems, and whether and how it can be made to work. The panel will also talk about best practices for implementing the approach in organizations.
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wandisco, wan, wide area network, hadoop, clusters, clustering, load balancing, data, big data, data storage, storage
    
WANdisco
Published By: Altiscale     Published Date: Dec 16, 2014
This webinar will explore the merits of the Hadoop SaaS approach, relative to competing cloud and on-premise Hadoop solutions that exist today.
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altiscale, hadoop, saas, hadoop saas, cloud service
    
Altiscale
Published By: Cask     Published Date: Feb 04, 2015
It’s time for business applications to include Big Data functionality, and it’s time for developers to get on the Big Data train. This webinar will focus on how to make that happen.
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cask, hadoop, big data, application development, big data application development
    
Cask
Published By: General Atomics     Published Date: Jan 13, 2015
The term “Big Data” has become virtually synonymous with “schema on read” unstructured data analysis and handling techniques like Hadoop. These “schema on read” techniques have been most famously exploited on relatively ephemeral human-readable data like retail trends, twitter sentiment, social network mining, log files, etc.
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general atomics, big data, metadata, nirvana, hadoop
    
General Atomics
Published By: Altiscale     Published Date: Mar 30, 2015
Implementing and scaling Hadoop to analyze large quantities of data is enormously complicated. Unforeseen, very challenging problems are to be expected. However, if you can learn to recognize the problems before a fire starts, you can prevent your hair (and your Hadoop implementation) from igniting. From the Hadoop experts at Altiscale, here are some of the danger signs and problems you should watch out for, as well as real-world lessons learned for heading them off.
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implementing and scaling, data analysis, hadoop scaling
    
Altiscale
Published By: Altiscale     Published Date: Mar 30, 2015
This industry analyst report describes important considerations when planning a Hadoop implementation. While some companies have the skill and the will to build, operate, and maintain large Hadoop clusters of their own, a growing number are choosing not to make investments in-house and are looking to the cloud. In this report Gigaom Research explores: • How large Hadoop clusters behave differently from the small groups of machines developers typically use to learn • What models are available for running a Hadoop cluster, and which is best for specific situations • What are the costs and benefits of using Hadoop-as-a-Service With Hadoop delivered as a Service from trusted providers such as Altiscale, companies are able to focus less on managing and optimizing Hadoop and more on the business insights Hadoop can deliver.
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hadoop implementation, hadoop cluster, hadoop-as-a-service, managing and optimizing
    
Altiscale
Published By: Altiscale     Published Date: May 28, 2015
Big Data technologies are maturing and quickly moving into the next phase - one that expands in data use-cases as Hadoop moves into more influential roles throughout IT infrastructures. In this just-released report, Gartner is recognizing four Big Data vendors as "cool." Gartner says these vendors can meaningfully and synergistically combine multiple types of functionality.
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big data, vendors, functionality, cool vendors, consistent performance, it management, data management; add - gartner, hadoop
    
Altiscale
Published By: Altiscale     Published Date: May 28, 2015
Altiscale’s Hadoop-as-a-Service can reduce costs and improve data scientist productivity, resulting in products, services, and insights realized sooner.
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hadoop-as-a-service, data improvement, data productivity, economic benefits, economic impact, big data, forrester
    
Altiscale
Published By: Altiscale     Published Date: Aug 25, 2015
Weren't able to attend Hadoop Summit 2015? No sweat. Learn more about the latest Big Data technologies in these technical presentations at this recent leading industry event. The Big Data experts at Altiscale - the leader in Big Data as a Service - have been busy at conferences. To see all four presentations (in slides and youtube video), click here. https://www.altiscale.com/educational-slide-kit-2015-big-data-conferences-nf/ • Managing Growth in Production Hadoop Deployments • Running Spark & MapReduce Together in Production • YARN and the Docker Ecosystem • 5 Tips for Building a Data Science Platform
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hadoop, hadoop technologies, hadoop information
    
Altiscale
Published By: Altiscale     Published Date: Aug 25, 2015
Hype abounds about Big Data. And it's hard to know how to effectively exploit its potential. Learn how to separate fact from fiction in this new webinar + research note titled "Amazon EMR is Easy and 7 Other Myths." If you're considering launching a new Big Data initiative, or if you are currently struggling with Amazon EMR, view this 30 minute on-demand webinar + research note and dispel the most common untruths about Amazon EMR, as determined by leading Hadoop experts. Specifically, you'll learn: • The differences between Hadoop-as-a-Service and Amazon EMR • Why Hadoop on Amazon is not elastic • Why EMR is not a "plug-n-play" application • How costs get out of control with Amazon EMR
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amazon emr, big data, haddop
    
Altiscale
Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache
    
Altiscale
Published By: IBM     Published Date: Oct 30, 2014
Filled with exotic terms like “Hadoop” and “Data Scientist”, Big Data, business intelligence and analytics have always been the domain of the biggest enterprises with huge teams to devote to analyzing data. But thanks to the latest technology advances businesses of almost any size can utilize tools to help inform every part of business decision-making. This SlashGuide looks at a recent Slashdot Pulse research study on BI/BA and discusses what’s really important when it comes to Big Data – and what businesses can do now to capitalize on the trend.
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business analytics, big data, bi/ba
    
IBM
Published By: IBM     Published Date: Apr 29, 2015
IBM InfoSphere BigInsights for Hadoop enables organizations to efficiently manage and mine large volumes of diverse data for valuable insights. IBM builds on a 100% Apache Hadoop foundation with common tools such as spreadsheets, R analytics and SQL access for greater usability.
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bigsheets, data management, business intelligence, workload optimization, data, sql
    
IBM
Published By: IBM     Published Date: May 22, 2015
This presentation will demonstrate that it is possible to turn the promise of Big Data into business value by applying predictive analytics to Big Data sources such as Hadoop, Cloudera, and BigInsights.
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big data, analytics, forecasting, data management, hadoop
    
IBM
Published 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
Published 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
Published 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
Tags : 
    
SAS
Published By: SAS     Published Date: Oct 18, 2017
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
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SAS
Published By: MapR Technologies     Published Date: Dec 12, 2013
Evaluator Group looks at what will make Hadoop an enterprise data center-grade analytics platform.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
Published By: MapR Technologies     Published Date: Dec 12, 2013
When used effectively, Hadoop can deliver unparalleled value in revealing new analytics-driven revenue streams, improving customer acquisition and retention, as well as increasing operational efficiencies. The Hadoop Buyer's Guide is an invaluable resource for those investigating or evaluating Hadoop---from understanding how Hadoop can solve your data challenges, to what to look for when selecting a solution, to comparing vendors, and preparing for implementation and future success. Download the guide, and get everything you need to know about choosing the right Hadoop distribution for your business success.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
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