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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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Qualification for a Programmer and the Degree You Need

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While the debate over the value of a computer science degree rages on, these programming leaders emphasize the importance of a well-rounded education, with plenty of time spent studying subjects beyond the console. The biggest challenges in life do not have technical fixes," Eich says, "so it's important to study history, literature, art, and other kinds of human knowledge than anything to do with computers. Hickey agrees, placing programming into a broader perspective. "Programming is a very new endeavor in the historical scheme of things," he says. "One shouldn't presume that we understand how best to pursue it. 1# Tips to become a programmer " Instead, Hickey suggests pursuing other educational interests to help understand the kinds of problems programming can solve."The best programmers are those that can understand, communicate about, and solve problems in the domains they are in," he says. "Software is just a tool for t...
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