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MapR M7: Enterprise-grade Platform for NoSQL and Hadoop
NoSQL databases are a compelling alternative to relational databases because of their capability to store and process large amounts of loosely structured data at fast speeds. The cost effectiveness of such solutions and the proliferation of new data sources have led to widespread adoption of NoSQL.
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MapR and LucidWorks: Crowd Sourcing Reflected Intelligence Using Search and Big Data
Search has evolved in recent years beyond keyword search into a more broadly applicable information discovery tool by using principles of reflected intelligence. Learn how several organizations combine big data, search and reflected intelligence to improve search results and decision-making, and how LucidWorks and MapR work together to make it possible for organizations to get started using reflected intelligence in their search applications.
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Kusnetzky Group: MapR Technologies M7 Making Big Data Work for Everyone
Big Data is emerging as an important tool to help organizations learn more about their business operations, product performance, and customer purchasing behavior. It is misunderstood by the media making it difficult for organizations to determine if investing in this tool will bring results and make it possible to improve efficiency, bring out better products and services or better understand customer requirements.
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Evaluator Group: Advancing Hadoop - MapR's M7 Edition
The number of enterprise-level deployments of Hadoop MapReduce is rising quickly, driven by a need to understand and potentially adopt this new business analytics platform for business applications. We note that pilot Hadoop projects are underway within many of the Fortune 1000 group of companies. Responding to this demand, the Hadoop ecosystem is now offering "enterprise" versions of Hadoop.
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Intelligent Business Strategies: Offloading and Accelerating Data Warehouse ETL Processing Using Hadoop
Mike Ferguson, Managing Director of Intelligent Business Strategies and former Chief Architect at Teradata, discusses offloading of ETL processing to a much lower cost Hadoop platform where it can scale to manage increasing transaction volumes as well as integrate this data with new more complex high value data types like clickstream, and un-modelled multi-structured data. Hadoop can be used as a long term data store for Big Data as well as archived data warehouse data and as an analytical platform to handle workloads that are unlikely to be done in traditional data warehouses.
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CITO Research: Choosing a Provider from the Hadoop Ecosystem
Enterprises are faced with new requirements for data. We now have big data that is different from the structured, cleansed corporate data repositories of the past. Before, we had to plan out structured queries. In the Hadoop world, we don’t have to sort data according to a predetermined schema when we collect it. We can store data as it arrives and decide what to do with it later. Today, there are different ways to analyze data collected in Hadoop—but which one is the best way forward?
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Deploying Big Data with MapR and StackIQ
A Simplified, Automated Solution for Enterprise Hadoop from StackIQ and MapR.
As Big Data analysis continues to penetrate the enterprise, the requirement for true enterprise-ready solutions continues to grow. These systems must be reliable, robust, scalable, and manageable within the enterprise IT framework.
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Quantifying the Value of MapR
MapR makes development, administration and end-user file access and insight much simpler and faster. Developed specifically for high availability and data protection, MapR provides assurance with 100% uptime for your business analytics process, recovery from user and application errors and strong protection against lost data.
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MapR White Paper: High Availability in the Hadoop Ecosystem
The MapR Distribution for Apache Hadoop provides high availability with no single points of failure across the entire stack. In the storage layer, MapR’s Distributed NameNode HATM architecture provides high availability with self-healing and support for multiple, simultaneous failures, with no additional hardware whatsoever.
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