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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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The AI Quick Tutorial Put You on Edge

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Artificial intelligence (AI) means different things to different people. Some believe that AI is synonymous with any form of intelligence achieved by nonliving systems; they maintain that it is not important if this intelligent behavior is not arrived at via the same mechanisms on which humans rely. For others, AI systems must be able to mimic human intelligence.


AI Tutorial

How Humans Accomplish Intelligence.


No one would argue with the premise that to study AI or to implement AI systems, it is helpful if we first understand how humans accomplish intelligent behavior; that is, we must understand activities that are deemed intelligent in an intellectual, scientific, psychological, and technical sense.


For example, if we want to build a robot capable of walking like a human, then we must first understand the process of walking from each of those perspectives; people, however, do not accomplish locomotion by constantly stating and following a prescribed set of formal rules that explain how to take steps. 


In fact, the more human experts are asked to explain how they achieve their level of performance in any discipline or endeavor, the more they are likely to fail. For example, when Israeli fighter pilots were asked to explain their prowess for flying, their performance actually declined.


Expert performance stems not from constant, conscious analysis but from the subconscious levels of the mind. Imagine trying to drive on an expressway during rush hour and needing to consciously weigh each vehicle-control decision.


Real Example of Intelligence in Humans.


Consider the story of the professor of mechanics and the unicyclist. If the professor is asked to cite principles of mechanics as he attempts to ride the unicycle and bases his success on the unicycle on knowing those principles, he is doomed to failure. 


Likewise, if the unicyclist attempts to learn the laws of mechanics and apply them while he performs his craft, he, too, is destined for failure and perhaps a tragic accident.


The root of Artificial Intelligence.


The point is, human skill and expertise in many disciplines seem to be developed and stored in the subconscious, rather than being available upon explicit request from memory or first principles.
What is AI?


AI is not natural but is man-made. the term artificial means synthetic (i.e., man-made) and generally has a negative connotation as being a lesser form of a real thing. Artificial objects are often superior to real or natural objects, however. 


Consider, for example, an artificial flower, an object made of silk and wire and arranged to resemble a bud or blossom.


20-Free Videos of Artificial Intelligence

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