Spark vs MapReduce Comparison
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Questions and Answers

What is the primary reason Apache Spark is considered faster than traditional MapReduce frameworks?

  • It uses more efficient hardware resources.
  • It minimizes data processing overhead by using directed acyclic graphs.
  • It integrates tightly with Hadoop's YARN.
  • It avoids disk I/O by caching intermediate data in memory. (correct)

    • Which of the following best describes the relationship between RDDs and DataFrames in Spark?

  • RDDs cannot be parallelized, but DataFrames can.
  • DataFrames are less efficient than RDDs for iterative algorithms.
  • DataFrames provide a higher-level abstraction over RDDs with schema information. (correct)
  • RDDs are optimized for relational operations, while DataFrames are not.

    • In the context of Spark Streaming, what is the purpose of a Discretized Stream (DStream)?

  • It breaks a stream of data into small batches for processing as RDDs. (correct)
  • It continuously computes results for an infinite stream of data.
  • It directly supports relational queries on streaming data.
  • It bypasses the use of RDDs for faster processing.

    • Which feature of Spark MLlib pipelines ensures that data preparation steps and model training can be reused and organized efficiently?

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

    What advantage does Apache Pig provide over raw MapReduce programming?

    <p>Pig abstracts complex data operations into simpler SQL-like queries. (D)</p> Signup and view all the answers

    How does Spark’s lazy evaluation improve the efficiency of data processing pipelines?

    <p>It prevents unnecessary computations by combining transformations. (A)</p> Signup and view all the answers

    The component of Apache Pig that converts Pig Latin scripts into MapReduce jobs for execution is called what?

    <p>Pig Compiler (B)</p> Signup and view all the answers

    Which feature distinguishes DataFrames from RDDs in terms of data handling capabilities?

    <p>DataFrames implement schema information for validation. (B)</p> Signup and view all the answers

    What is the function of a parser in the context of compiling?

    <p>To analyze the syntax of the source code. (B)</p> Signup and view all the answers

    In machine learning with Spark, what is the primary purpose of feature engineering?

    <p>To create new features from existing data. (A)</p> Signup and view all the answers

    Which deployment mode offers the driver running on the cluster rather than locally?

    <p>yarn-cluster (C)</p> Signup and view all the answers

    How does Spark SQL enhance data processing capabilities?

    <p>By enabling SQL queries on structured data. (D)</p> Signup and view all the answers

    What is the key role of the updateStateByKey transformation in Spark Streaming?

    <p>To accumulate state across time intervals. (C)</p> Signup and view all the answers

    What result does the DESCRIBE command produce in Pig Latin?

    <p>It provides the schema of a dataset. (B)</p> Signup and view all the answers

    What is a major advantage of using k-fold cross-validation during hyperparameter tuning?

    <p>It allows for consistent training-test splits. (B)</p> Signup and view all the answers

    In Spark Streaming, what is the main purpose of checkpointing?

    <p>To secure metadata and facilitate recovery. (B)</p> Signup and view all the answers

    Which transformation in Apache Pig creates a bag for each key with all matching records?

    <p>COGROUP; it groups records based on keys. (C)</p> Signup and view all the answers

    What type of join does the JOIN transformation perform in Apache Pig?

    <p>Inner join by default. (B)</p> Signup and view all the answers

    Flashcards

    Compiler role

    A compiler translates source code into machine code.

    Spark VectorAssembler

    Combines multiple features into a single vector.

    Yarn-client vs. yarn-cluster

    Yarn-client driver runs locally; yarn-cluster runs on the cluster.

    Spark SQL purpose

    Allows querying structured data using SQL-like interface.

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    updateStateByKey

    Performs stateful computations across multiple time windows (streaming).

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    Pig DESCRIBE

    Displays the schema of a relation.

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    k-fold cross-validation

    Splits data into 'k' parts for training and validation (repeatedly).

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    Spark Streaming Checkpoint

    Saves metadata/state to recovery on failure.

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    Pig JOIN default

    Performs an inner join based on common keys.

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    Feature engineering in Spark

    Creating and preparing features for machine learning models.

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    Spark's speed advantage over MapReduce

    Spark leverages in-memory data caching to avoid disk I/O, significantly speeding up data processing compared to MapReduce, which often involves repeated disk reads.

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    RDD vs. DataFrame in Spark

    DataFrames are higher-level abstractions built on top of RDDs in Spark. They provide schema information and are optimized for relational operations.

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    DStream purpose

    A Discretized Stream (DStream) in Spark Streaming breaks a continuous data stream into batches for processing as Resilient Distributed Datasets (RDDs), making processing easier.

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    Spark MLlib PipelineModel

    Spark's PipelineModel allows for efficient reuse and organization of data preparation steps and model training in machine learning workflows.

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    Pig over MapReduce

    Apache Pig simplifies complex data operations with a SQL-like language. It abstracts away low-level MapReduce programming complexities.

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    Spark's lazy evaluation

    Spark's lazy evaluation optimizes processing by postponing computations until necessary, combining transformations efficiently and preventing unnecessary intermediate data processing.

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    Pig Latin script translation

    Apache Pig's interpreter converts Pig Latin scripts into MapReduce jobs, allowing them to run on Hadoop clusters.

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    Spark's Efficiency

    Spark's in-memory processing and lazy evaluation optimize data processing, enabling significantly faster query performance and lower data movements than traditional disk-centric methods.

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    Study Notes

    Spark and MapReduce Comparison

    • Spark is faster than traditional MapReduce due to caching intermediate data in memory, avoiding disk I/O.

    RDDs and DataFrames in Spark

    • DataFrames offer a higher-level abstraction over RDDs, incorporating schema information.

    Spark Streaming and DStreams

    • DStreams break streaming data into batches (RDDs) for processing.

    Spark MLlib Pipelines

    • PipelineModel encompasses data preparation and model training steps for reusability.

    Apache Pig Advantages

    • Pig Latin abstracts complex data operations into simpler SQL-like queries.

    Spark's Lazy Evaluation

    • Lazy evaluation combines transformations, preventing unnecessary computations.

    Apache Pig Compilation

    • A compiler translates Pig Latin scripts to MapReduce jobs.

    Spark MLlib Feature Engineering

    • Feature engineering involves creating a VectorAssembler.

    Spark Deployment Modes

    • yarn-client runs the driver locally, while yarn-cluster runs it on the cluster.

    Spark SQL

    • Spark SQL enables querying structured data using SQL-like interface.

    Spark Streaming State Management

    • updateStateByKey allows stateful computations across time windows in Spark Streaming.

    Pig Latin Querying

    • DESCRIBE displays schema of relations or bags in Pig Latin.

    Cross-Validation for Hyperparameter Tuning

    • K-fold cross-validation uses every data point for both training and validation in each iteration.

    Spark Streaming Fault Tolerance

    • Checkpointing saves metadata and state to ensure recovery.

    Pig Latin Data Combination

    • JOIN in Pig Latin performs an inner join on matching keys by default.

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    Description

    This quiz explores the differences between Apache Spark and traditional MapReduce. It covers key concepts such as RDDs, DataFrames, DStreams, and the advantages of using Spark for data processing. Test your knowledge on Spark's features, lazy evaluation, and deployment modes.

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