Apache Spark Lecture Quiz

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Questions and Answers

What is one of the main features Spark offers for speed?

  • Ability to run computations in memory (correct)
  • Ability to run computations on disk
  • Ability to support only iterative algorithms
  • Ability to support only batch applications

What types of computations can Spark efficiently support?

  • Interactive queries and batch applications
  • Interactive queries and stream processing (correct)
  • Batch applications and iterative algorithms
  • Stream processing and iterative algorithms

What does Spark make easy and inexpensive in production data analysis pipelines?

  • Supporting only iterative algorithms
  • Supporting only batch applications
  • Running computations on disk
  • Combining different processing types (correct)

What workloads does Spark cover?

<p>Batch applications, iterative algorithms, interactive queries, and streaming (D)</p> Signup and view all the answers

What is Spark designed to be?

<p>Fast and general purpose cluster computing platform (B)</p> Signup and view all the answers

What is one of the key features of Apache Spark?

<p>Speed (D)</p> Signup and view all the answers

In which languages does Spark provide high-level APIs?

<p>Java, Scala, Python, and R (A)</p> Signup and view all the answers

What does Spark's Resilient Distributed Datasets (RDDs) allow for?

<p>Faster iterative and interactive processing (C)</p> Signup and view all the answers

What is a primary goal of Apache Spark's design?

<p>Being fast, flexible, and easy to use (C)</p> Signup and view all the answers

What additional features does Spark offer beyond Hadoop MapReduce?

<p>Various types of data processing workloads (A)</p> Signup and view all the answers

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Study Notes

Spark Features and Capabilities

  • One of the main features Spark offers for speed is in-memory computing, which allows for faster processing and analysis of data.
  • Spark efficiently supports batch processing, interactive queries, and real-time stream processing, making it a versatile tool for various computation types.
  • Spark makes easy and inexpensive in production data analysis pipelines by providing a unified engine that can handle a wide range of data processing tasks.
  • Spark covers a wide range of workloads, including batch processing, interactive queries, and real-time stream processing, making it a comprehensive tool for data analysis.
  • Spark is designed to be fast, flexible, and extensible, making it a powerful tool for big data analysis.

Spark APIs and RDDs

  • Spark provides high-level APIs in Java, Python, Scala, and R, allowing developers to work in their preferred language.
  • Spark's Resilient Distributed Datasets (RDDs) allow for fault-tolerant and parallel processing of data, making it easy to work with large datasets.

Spark Design and Goals

  • A primary goal of Apache Spark's design is to provide a unified engine for big data processing that can handle a wide range of data processing tasks.
  • Spark offers additional features beyond Hadoop MapReduce, including in-memory computing, interactive queries, and real-time stream processing, making it a more comprehensive tool for big data analysis.

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