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Big data can become a key competitive weapon for organizations -- if they can successfully implement systems and processes for analyzing their growing vaults of both structured and unstructured data. In-memory analytics tools are one possible avenue to go down. Doing data analysis in memory reduces disk I/O bottlenecks and increases analytical flexibility, two potential pluses in pursuing big data analytics. In this handbook, readers will learn about the benefits and challenges of mixing big data and in-memory analytics; they’ll also get advice on deciding whether the in-memory approach is right for their organizations and tips on building a technology architecture that can support in-memory processing. Access >>>
Table of contents
- In-memory analytics tools pack big data punch
- In-memory, big data combo needs solid IT foundation
- Faster analytics speed not enough on in-memory apps
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