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De-Risk Hadoop to AWS Migration with a Data-First Approach

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Spark Muscles Into Big Data Processing

By: TechTarget Data Management

Type: eBook

It comes as no great surprise that the Apache Spark architecture has been horning in on the batch processing domain once controlled by Hadoop's MapReduce. But that's only part of the story. With data processing, streaming and machine learning capabilities on its résumé, the open source engine is learning to get along entirely without Hadoop in certain applications. In fact, one industry analyst cautiously sees a day when Spark could declare total independence, potentially bust up Hadoop cluster dominance and link separately with other Apache technologies. 

In this three-part handbook, senior news writer Jack Vaughan examines the distinct advantages the Apache Spark architecture has over MapReduce. Also highlighted is how Spark's ability to process and analyze streaming data is helping detect fraudulent activities at a major banking and credit-card company. Next, Spark 2.0's upcoming upgrades to analytics speed, machine learning libraries, SQL support and stream processing are detailed. To close, Vaughan and senior news writer Ed Burns look at combining Spark and NoSQL databases in operational analytics applications, which could help broaden the use of both technologies.

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    Big data analytics: Data warehouse vs Hadoop

    By: SAS

    Type: eGuide

    The traditional data warehouse is often not as cost-effective as Hadoop when it comes to data storage for big data analytics. But Hadoop isn’t a good fit for every business intelligence project—for instance, real-time analytics.

    When looking at the big picture, Hadoop isn’t an effective replacement for your enterprise data warehouse. So how can you get the best of both worlds?

    This expert e-guide explores how Hadoop and the data warehouse can complement each other on business intelligence projects to help you gain strategic insights.   

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    5 steps to maximize the value of Hadoop

    By: SAS

    Type: White Paper

    Many organizations are struggling to implement Hadoop. For example, it can be difficult to find personnel that understand programming languages like Sqoop, Hive, Pig, and MapReduce in order to productively use Hadoop.

    This resource describes the 5 critical steps to maximize the value of Hadoop before embarking on a big data project, including:

    • Evaluate if and how Hadoop fits into your existing data and analytics architecture
    • Assess skill/talent gaps early
    • And 3 more

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