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  • Big data queries eyed to head off Hadoop performance problems

    Processing in big data systems can slow to a crawl if queries are not properly tuned or workloads not well balanced -- issues that call for careful monitoring of clusters. Continue Reading

  • Big data initiatives get huge boost from new technologies

    Big data initiatives can help companies improve operational efficiency, create new revenue and gain a competitive advantage. But traditional data processing often can't deal with the mountains of structured, semi-structured and unstructured data that needs to be mined for value. That leaves big data initiatives hungry for new tools and technologies to ease and speed data processing and predictive analytics functions.

    In this e-book, get insight on useful tools for big data projects. The first chapter provides real-world examples of organizations using SQL-on-Hadoop engines to simplify the process of querying and analyzing Hadoop data. The second defines Spark -- including its capabilities and limitations -- and offers advice on deploying, managing and using the big data processing engine. And the third chapter focuses on using the open source R analytical programming language and commercial tools such as SAS and IBM SPSS to run analytical applications against Hadoop data sets.

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