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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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Scala: Unique features you need to know

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Let us start it in layman terms... Why you need Scala... The name Scala stands for “scalable language.” The language is so named because it was designed to grow with the demands of its users. Where Scala can be applied... You can apply Scala to a wide range of programming tasks, from writing small scripts to building large systems.   The real use of Scala... Scala is easy to get into. It runs on the standard Java platform and interoperates seamlessly with all Java libraries. It’s quite a good language for writing scripts that pull together Java components. But it can apply its strengths even more when used for building large systems and frameworks of reusable components. Technically, Scala is a blend of object-oriented and functional programming concepts in a statically typed language. The fusion of object-oriented  and functional programming shows up in many different aspects of Scala; It is probably more pervasive t...
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