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.
Generative Adversarial Networks (GANs) are artificial intelligence models that generate realistic data by training two neural networks (generator and discriminator) in a competing learning mechanism. Many variants of this technology have been developed for different use cases.
Data replication is the process of moving data from one place to another, copying it, or storing data in more than one place at the same time.
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.
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