Separación de Computación y Almacenamiento en proyectos Hadoop 3.0. Por BlueData. - Seguridad Mania.com - España y América Latina

Join this webinar to learn how separating compute from storage for Big Data delivers greater efficiency and cost savings. Historically, Big Data deployments dictated the co-location of compute and storage on the same physical server. Data locality (i.e. moving computation to the data) was one of the fundamental architectural concepts of Hadoop. But this assumption has changed – due to the evolution of modern infrastructure, new Big Data processing frameworks, and cloud computing.

By decoupling compute from storage, you can improve agility and reduce costs for your Big Data deployment. In this webinar, we’ll discuss how: - Changes introduced in Hadoop 3.0 demonstrate that the traditional Hadoop deployment model is changing - New projects by the open source community and Hadoop distribution vendors give further evidence to this trend - By separating analytical processing from data storage, you can eliminate the cost and risks of data duplication - Scaling compute and storage independently can lead to higher utilization and cost efficiency for Big Data workloads Don’t miss this webinar. Learn how the traditional Big Data architecture is changing, and what this means for your organization. REGISTER TODAY!