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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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Quick Guide: Machine Learning Examples and Uses

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I want to share with you the best real-time examples on machine learning.  Because of new computing technologies, machine learning today is not like machine learning of the past.  While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Machine learning use cases The heavily hyped, self-driving Google car? The essence of machine learning.  Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.  Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.  Fraud detection? One of the more obvious, important uses in our world today. Best example : "pattern recognition" is best example for Machine Learning Related: Take Quiz on Machine Learning Where can you apply mach...
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