Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
Explore the world of Internet of Things (IoT), a powerful technology that is reshaping our lifestyles, work systems, and industries. Learn what IoT means, its applications, advantages and key role in driving the fourth industrial revolution.
Explore the evolving world of Data Warehouse Modernization and its importance in leveraging big data. Learn how data warehouses work, their types, requirements in various industries, and application areas.
Neural Style Transfer (NST) is a method of applying the style of one image to another using artificial neural networks. Using deep learning algorithms, this technique combines two images: the style of one (e.g. a work of art) and the content of the other (e.g. a photograph) to create an expressive and artistic result.
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