Answer / Chandan Singh

The common workflow of a Spark program consists of the following steps:n1. Creating a SparkContext: This is the entry point to the Spark system and establishes a connection between the application and the cluster.n2. Reading data: Data can be read from various file systems or external sources using Spark's built-in APIs like Spark SQL, DataFrames, or Resilient Distributed Datasets (RDDs).n3. Transformations: Perform transformations on the data by applying various functions to create new datasets.n4. Actions: Convert transformations into concrete results by calling an action function such as `count()`, `collect()`, or `saveAsTextFile()`.n5. Caching and Persistence: Cache frequently accessed datasets for faster processing in subsequent iterations or persist them on disk to free up memory.

When we create an rdd, does it bring the data and load it into the memory?

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Can I install spark on windows?

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What is the point of apache spark?

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What is sparkcontext in spark?

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Explain about the different types of trformations on dstreams?

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Why does the picture of Spark come into existence?

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Why apache spark is faster than hadoop?

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How is spark different from hadoop?

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Illustrate some demerits of using Spark.

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What is action, how it process data in apache spark

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Which language is better for spark?

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What is difference between map and flatmap in spark?

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