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.
Latent Dirichlet Allocation (LDA) is a topic modeling technique that allows the discovery of hidden topic structures on large amounts of text data.
Diffusion Models are models that have recently gained great interest in the field of machine learning and artificial intelligence, especially in image production. Diffusion models work by modeling noise on data to create realistic images.
Robotic Process Automation is defined as a technology that enables rule-based and repetitive business processes to be performed automatically by software robots.
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