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Pepperdata in Multi-tenant Environments

Published 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

Pepperdata on a Highly-tuned Hadoop Cluster

Published 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

Predictions 2015: Hadoop Will Become A Cornerstone Of Your Business Technology Agenda

Published By: Teradata     Published Date: Jan 30, 2015
Hadoop is the rising star of the business technology agenda for a simple reason — it disrupts the economics of data, analytics, and someday soon, all enterprise applications; it is secretly becoming an application platform too. Application development and delivery (AD&D) professionals should be aware of and take action on these eight predictions, including the disruptive power of “Hadooponomics,” Hadoop’s current killer app, the closing data management gap, and the emergence of brand new distros.
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hadoop, hadoop adoption, cornerstone, business technology, hadooponomics, enterprise adoption, applications, data management, best practices, business intelligence
    
Teradata

Productionizing Hadoop: Seven Architectural Best Practices

Published By: MapR Technologies     Published Date: Jan 08, 2014
Forrester Research shares seven architectural qualities for evaluating Big Data production platforms. In this webinar guest speaker Mike Gualtieri, Principal Analyst at Forrester, along with experts from MapR and Cisco, will present the following: • The 7 architectural qualities for productionizing Hadoop successfully • Architectural best practices for Big Data applications • The benefits of planning for scale • How Cisco IT is using best practices for their Big Data applications Speakers • Mike Gualtieri, Principal Analyst at Forrester Research • Jack Norris, Chief Marketing Officer at MapR Technologies • Andrew Blaisdell, Product Marketing Manager at Cisco • Sudharshan Seerapu, IT Engineer at Cisco
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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, infrastructure management
    
MapR Technologies

Scaling Data Integration to Handle Big Data

Published By: IBM     Published Date: Oct 19, 2015
IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, infosphere, data profiling, business glossary, partition, pipeline parallelism, information server, data sources, hadoop, networking, security, data management, business technology
    
IBM

Scaling Data Integration to Handle Big Data

Published By: IBM     Published Date: Jan 13, 2016
IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, data integration, infosphere, information server, partition, data profiling, knowledge management, data management, business technology, data center
    
IBM

Scaling Data Integration to Handle Big Data

Published By: IBM     Published Date: Oct 13, 2016
IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, data, analytics, big data, data integration, data management, business technology, data center
    
IBM

Seven Reasons You Need Predictive Analytics Today

Published By: IBM     Published Date: Feb 03, 2016
Learn why advanced analytics tools are essential to sustain a competitive advantage. This white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics.
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ibm, data management, apache, hadoop, analytics
    
IBM

Shared Storage Offers Lower TCO than Direct-Attached Storage for Hadoop and NoSQL Deployments

Published 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

Significantly Improve Productivity in the Hadoop Data Lake

Published By: Teradata     Published Date: May 01, 2015
Creating value in your enterprise undoubtedly creates competitive advantage. Making sense of the data that is pouring into the data lake, accelerating the value of the data, and being able to manage that data effectively is a game-changer. Michael Lang explores how to achieve this success in “Data Preparation in the Hadoop Data Lake.” Enterprises experiencing success with data preparation acknowledge its three essential competencies: structuring, exploring, and transforming. Teradata Loom offers a new approach by enabling enterprises to get value from the data lake with an interactive method for preparing big data incrementally and iteratively. As the first complete data management solution for Hadoop, Teradata Loom enables enterprises to benefit from better and faster insights from a continuous data science workflow, improving productivity and business value. To learn more about how Teradata Loom can help improve productivity in the Hadoop Data Lake, download this report now.
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data management, productivity, hadoop, interactive, enterprise, enterprise applications
    
Teradata

Simplify Your Big Data Journey with a Tested and Validated Hadoop Solution

Published By: Dell EMC     Published Date: Nov 09, 2015
While the EDW plays an all-important role in the effort to leverage big data to drive business value, it is not without its challenges. In particular, the typical EDW is being pushed to its limits by the volume, velocity and variety of data. Download this whitepaper and see how the Dell™ | Cloudera™ | Syncsort™ Data Warehouse Optimization – ETL Offload Reference Architecture can help.
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Dell EMC

Simplifying Big Data Management, by Kusnetzky Group

Published By: MapR Technologies     Published Date: Dec 12, 2013
This independent whitepaper from the Kusnetzky Group Analyst describes the promise and challenges surrounding Big Data. It also validates the M7 solution from MapR, which simplifies big data management by consolidating disparate solutions into a single, enterprise-ready 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, analytical applications
    
MapR Technologies

Six Guiding Principles for Effective Data Lake Pipelines

Published By: Attunity     Published Date: Nov 15, 2018
IT departments today face serious data integration hurdles when adopting and managing a Hadoop-based data lake. Many lack the ETL and Hadoop coding skills required to replicate data across these large environments. In this whitepaper, learn how you can provide automated Data Lake pipelines that accelerate and streamline your data lake ingestion efforts, enabling IT to deliver more data, ready for agile analytics, to the business.
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Attunity

Solving the Big Data Intention-Deployment Gap

Published By: BlueData     Published Date: Aug 19, 2015
Big Data is on virtually every enterprise’s to-do list these days. Recognizing both its potential and competitive advantage, companies are aligning a vast array of resources to access and analyze this strategic asset. However, despite best intentions, the majority of these Big Data initiatives are either extremely slow in their implementation or are not yielding the results and benefits that enterprises expect. Download this white paper to learn how to solve the Big Data intention-deployment gap and see how you can make your infrastructure in a flexible, easy-to-use platform that will provide in-depth analytics.
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big data, big data intention-deployment, in-depth analytics, hadoop, data management, data center
    
BlueData

Splice Machine: SQL-on-Hadoop® Evaluation Guide

Published By: Splice Machine     Published Date: Feb 17, 2014
Hadoop has become popular to store massive data sets because it can distribute them across inexpensive commodity servers. Click here to learn more from this free report.
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big data, hadoop, data centers, massive data, servers, virutalization, blades, cloud, splice, splice machine, backup and recovery, blade servers, storage management, storage virtualization, cloud computing, data center design and management, colocation and web hosting
    
Splice Machine

Statistics and Machine Learning at Scale

Published 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

Tackling the Big Data Deluge in Science with Metadata

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, storage area networks, storage virtualization, server virtualization, data warehousing, data deduplication, data center design and management, colocation and web hosting
    
General Atomics

TDWI Best Practices Report: Next-Generation Analytics and Platforms

Published 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.
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pentaho, analytics, platforms, hadoop, big data, predictive analytics, networking, it management, knowledge management, data management
    
Pentaho

TDWI Best Practices Report: Next-Generation Analytics and Platforms

Published 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.
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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

TDWI Best Practices: Data Lakes, Purposes, Practices, Patterns and Platforms (customized reports)

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
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SAS

TDWI Best Practices: Improving Data Prep for BA

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

TDWI Data Warehouse Modernization in the Age of Big Data Analytics

Published By: Pentaho     Published Date: Aug 22, 2016
This white paper covers the many options available for modernizing a data warehouse.
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big data, data integration, bi systems, hadoop
    
Pentaho

Technology Insight Paper: Deploying Hadoop in Your Data Center

Published By: MapR Technologies     Published Date: Jan 03, 2014
As the demand for Big Data analytics mushrooms, IT decision-makers must prepare for the widespread deployment of Hadoop. This Technical Insight Paper from the Evaluator Group outlines the key requirements that must be met to make Hadoop enterprise data center ready.
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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, infrastructure management
    
MapR Technologies

Ten Warning Signs You Need Better Apache™ Hadoop® Security and Data Governance

Published By: Hortonworks     Published Date: Apr 05, 2016
This white paper will help you evaluate your ability to protect your data in a Apache Hadoop ecosystem. Read on to learn ten signs that you might need to improve security and data governance in order to manage risk while getting more value out of your Apache Hadoop environment.
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Hortonworks
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