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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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2 Top Real-time AI New Approaches for Learners

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Artificial Intelligence (AI) applications in real-time have two approaches. Those are Applied and Generalized. This post tells you the differences between these two.

Artificial Intelligence (AI).

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider smart.

How the AI started.  A machine, which has reproducing capabilities, basic arithmetic, and memory are called logical-machines. As enter into a new era, a new thought process created now called AI (Artificial Intelligence)

2 AI Real-Time Top Approaches for New Learners
AI Approaches

Artificial intelligence has two approaches - Applied and Generalized. 

  1. Applied Artificial Intelligence.  The Applied AI is, you can find in systems designed to trade the Stocks or maneuver an autonomous vehicle would fall into this category. 
  2. Generalized Artificial Intelligence.  Systems or devices that can, in theory, handle any task – are less common, but this is where some of the most exciting advancements are happening today.

Machine Learning (ML).

The father of Machine learning is Arthur Samuel. Machine Learning; the vehicle which is driving AI development forward with the speed it currently has.

Machine Learning, two reasons to invent it.
  • Rather than teaching computers everything they need to know about the world and how to do the tasks, instead, it might be possible to teach them to learn themselves. 
  • The second, more recently, was the emergence of the Internet and the increase in producing the amount of digital information.

Summary.

What do you need to do? Code logic in such a way that machines to act intelligently without human intervention. It is the funda of ML.

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