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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 in Web Development Read Now

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What is Scala - Scala's design has been influenced by many programming languages and ideas in programming language research. Beginner Notes on SCALA. In fact, only a few features of Scala are genuinely new; most have been already applied in some form in other languages. Scala's innovations come primarily from how its constructs are put together. At the surface level, Scala adopts a large part of the syntax of Java and C#, which in turn borrowed most of their syntactic conventions from C and C++. Expressions, Statements, and blocks are mostly as in Java, as is the syntax of classes, packages, and imports.  Besides syntax, Scala adopts other elements of Java, such as its basic types, its class libraries, and its execution model. Scala's new version. Scala also owes much to other languages. Its uniform object model was pioneered by Smalltalk and taken up subsequently by Ruby.  Its idea of universal nesting (almost every construct in Scala can be nested inside any...
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