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Now, let's shift gears and talk about **Spark**. If Hadoop is the granddaddy, then Spark is the younger, faster sibling. Spark was created to address some of the performance limitations of Hadoop's MapReduce. It's an open-source, distributed processing system designed for speed. Unlike Hadoop, which relies on disk I/O for intermediate results, Spark keeps data in memory whenever possible, significantly reducing processing time. This in-memory processing is a game-changer, making Spark much faster, especially for iterative algorithms and interactive data analysis. Spark's core abstraction is the **Resilient Distributed Dataset (RDD)**, which represents a collection of data partitioned across the cluster. RDDs are immutable, meaning they can't be changed once created, which simplifies data management and helps with fault tolerance. Spark also offers a rich set of APIs for various programming languages like Java, Scala, Python, and R, making it accessible to a wide range of developers.
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* ***Trademarks:*** These are symbols, logos, or brand names that distinguish goods or services from one another. Think of the Nike swoosh, the Apple logo, or the Coca-Cola script – all trademarks. They help consumers identify the source of goods or services and build brand recognition. Trademarks must be registered to be legally protected. Registering a trademark gives you legal rights over the brand name or logo. This helps consumers to identify the source of a product.
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