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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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Human Intelligence Vs Artificial Intelligence

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What is artificial intelligence
What is artificial intelligence

Key differences of Human and Artificial Intelligence

Human Intelligence: A leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''.

I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.

Artificial Intelligence: Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out.

The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.

Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.

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