RDDs Vs DataFrames under 60 seconds| Handle Distributed Data| Low-level Vs Higher-level Spark APIs

RDDs Vs DataFrames under 60 seconds |Handle Distributed Data |Low-level Vs Higher-level Spark APIs #interview #question

In this video, we will understand the how to work on distributed Data -RDDs (Resilient Distributed Datasets) and DataFrames are two fundamental abstractions in Apache Spark for working with distributed data.

RDDs provide flexibility and control, DataFrames offer ease of use, performance optimizations, and schema enforcement, making them the preferred choice for most structured data processing tasks in Apache Spark. However, RDDs still have their place in specialized use cases or scenarios where fine-grained control over data processing is required.

Most commonly asked interview questions when you are applying for any data based roles such as data analyst, data engineer, data scientist or data manager.

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Видео RDDs Vs DataFrames under 60 seconds| Handle Distributed Data| Low-level Vs Higher-level Spark APIs канала Sumit Mittal