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Scaling Up versus Scaling Out: Mythbusting Database Deployment Options for Big Data

December 19, 2018

In the world of Big Data, scaling out the database is the norm. As a result, Big Data deployments are often awash in clusters of small nodes, which bring with them many hidden costs. For example, scaling out usually comes at the cost of resource efficiency, since it can lead to low resource utilization. So why is scaling out so common? Scaled out deployment architectures are based on several assumptions that we’ll examine in this white paper. We’ll demonstrate that these assumptions prove unfounded for database infrastructure that is able to take advantage of the rich computing resources available on large nodes.

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Wonderflow helps world's best brands identify areas of improvement of their products and services. Our top-class text analysis technology extracts actionable insights from any kind of unstructured text, and contributes to reach high Customer Experience levels. Wonderflow can ingest hundreds of different data sources, from consumer reviews, to customer service records or social data. We support several latin and non-latin languages. The setup time of the Wonderboard is up to 80% shorter than the market average, while the accuracy of our analysis has proven to outperform any other text-mining company (>90% for English and German).

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Scaling Up versus Scaling Out: Mythbusting Database Deployment Options for Big Data