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.
Data matching is the process of linking a data field from one source to a data field from another source.
Cross-Attention is a powerful mechanism for sharing information between different datasets or different modalities (e.g. text and image) in artificial intelligence, especially in generative AI models.
Supply chain management refers to the optimization of the flow from the supply of raw materials of a product to its production, from the logistics process to its delivery to the final customer.
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