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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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Story IoT devices human intelligence basic concepts(1 of 3)

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The definition of Artificial intelligence is the continuous technological advances have gradually surrounded people with a wide range of electronic devices and information technology.
The short name for Artificial intelligence is 'ai'.
In this regard, it is necessary to develop intuitive interfaces and systems with some degree of intelligence, with the ability to recognize and respond to the needs of individuals in a discrete and often invisible way, considering people in the center of the development to create technologically complex and intelligent environments.
artificial intelligence

Artificial Intelligence Vs Ambient Intelligence

  • Artificial Intelligence
  • Ambient Intelligence

1. Artificial Intelligence

  • Artificial intelligence in basic words machines can take decision intelligently
  • They understand natural language - How humans communicate each other
  • Robots - you can treat them as Artificial intelligent machines.
  • Machine Language Algorithms help machines act as intelligent machines.

2. Ambient Intelligence (AmI)

It is an emerging multidisciplinary area based on ubiquitous computing, which influences the design of protocols, communications, systems, devices, etc., proposing new ways of interaction between people and technology, adapting them to the needs of individuals and their environment (Weber, et al. 2005). 

AmI offers a great potential to improve quality of life and simplify the use of technology by offering a wider range of personalized services and providing users with easier and more efficient ways to communicate and interact with other people and systems (Weber, et al., 2005; Corchado, et al., 2008b). 

However, the development of systems that clearly fulfill the needs of AmI is difficult and not always satisfactory. It requires a joint development of models, techniques and technologies based on services.

An AmI-based system consists of a set of human actors and adaptive mechanisms which work together in a distributed way. Those mechanisms provide on demand personalized services and stimulate users through their environment according to specific situation characteristics (Weber, et al., 2005).

AI problems tend to be large. They are computationally complex and cannot be solved by straightforward algorithms. 

AI problems and their domains tend to embody a large amount of human expertise, especially if tackled by strong AI methods. Some types of problems are better solved using AI, whereas others are more suitable for traditional computer science approaches involving simple decision-making or exact computations to produce solutions.

Let us consider a few examples:
  • Medical diagnosis - You can use AI in medical field. Devices that used in Radiology, you can use as an aid to specialist Doctor.
  • Banking Applications- Chatbots are famous AI based tools.

Salary of Artificial Intelligence Engineers

According to Indeed.com, the average IT salary – the keyword is "artificial intelligence engineer" – in the San Francisco area ranges from approximately $134,135 per year for software engineer to $169,930 per year for machine learning engineer.

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