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 science is about gaining actionable insights into the data a company owns, supported by a variety of applications.
NLP tokenization (NLP Tokenization) is the process of dividing raw text into “tokens”, which are small units that can be processed by machine learning models in natural language processing.
Post-quantum cryptography is the process of developing encryption algorithms and cryptographic systems designed to be secure against both classical and quantum computers.
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