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.
Penetrasyon, genellikle bir şeyin içine nüfuz etme ya da giriş yapma anlamında kullanılan bir terimdir.
The data lake is where long-term data containers gather that capture, clean, and explore any raw data format at scale. Data subsets are powered by low-cost technologies that many downstream possibilities can benefit from, including data warehouses, and recommendation engines.
Fine-tuning is the process of optimizing a pre-trained model for a specific task. This method is an important part of the approach known as transfer learning and is widely used in modern artificial intelligence projects.
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