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.
Prompt engineering is the process of designing correct guidance and instructions (prompts) to get the best results from big language models (LLM) and AI systems. The power of AI models relies on their ability to produce accurate results with given input.
Data integration is a complex process by which data from different data sources and IT systems of a company is combined, enhanced, enriched and cleaned
Neural Networks are one of the fundamental building blocks of artificial intelligence and machine learning. Inspired by the functioning of the human brain, these structures are used in solving complex problems and data processing.
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