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Big data and Amazon Web Services may go well together, but what are the steps to getting started? How much of an investment -- time, people, power and money -- are big data projects? With data this big, fast-changing and varied, most traditional approaches to data management won’t hack it. And even the biggest multinationals couldn’t shoulder the on-premises costs.
In this handbook, readers will learn about the technologies needed for big data analytics -- such as Kinesis, Hadoop and Elastic MapReduce -- and how it works in AWS. Cloud expert David Linthicum takes a look at the cost-effective approaches to big data AWS is offering -- and how they can benefit your organization. Next, Matt Wood, general manager of data science at AWS, answers questions about the changes AWS has made to its cloud in recent years. The changes -- 42 price cuts among them -- have raised this question: Just how low can pricing go? Wood also discusses a host of market trends in data science and analytics. Finally, consultant Tom Nolle looks at the future of the cloud -- specifically the addition of hosted services, such as AppStream and Kinesis, as enterprise and developer problem solvers. Access >>>
Table of contents
- What's what in AWS big data analytics
- AWS chief data scientist: It’s all for the customer
- AppStream and Kinesis change the cloud game
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