Big Data Concepts and Workload Processing
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What is the main characteristic of parallel data processing?

  • It requires a single sequential execution of tasks.
  • It divides a large task into smaller sub-tasks that run simultaneously. (correct)
  • It relies solely on manual processing of data.
  • It only applies to small-scale data operations.

    • What kind of framework is Hadoop?

  • A proprietary framework for data analysis
  • A cloud-based platform for real-time data streaming
  • A specialized database management system
  • An open-source framework for large-scale data storage and processing (correct)

    • How does parallel data processing enhance task execution?

  • By eliminating the need for processing completely.
  • By allowing tasks to run in a linear sequence.
  • By executing multiple sub-tasks at the same time. (correct)
  • By reducing the amount of data to be processed.

    • Which of the following best describes the primary purpose of Hadoop?

    <p>To facilitate large-scale data storage and processing</p> Signup and view all the answers

    Which scenario exemplifies parallel data processing?

    <p>Running multiple simulations for different customer scenarios simultaneously.</p> Signup and view all the answers

    What is a key characteristic of Hadoop?

    <p>It supports distributed storage and processing</p> Signup and view all the answers

    Which of these features is NOT associated with Hadoop?

    <p>Real-time data processing capabilities</p> Signup and view all the answers

    What is NOT a benefit of parallel data processing?

    <p>Greater task complexity due to synchronization issues.</p> Signup and view all the answers

    What is the primary focus of parallel data processing?

    <p>Executing multiple subordinate tasks simultaneously</p> Signup and view all the answers

    What type of systems is Hadoop typically run on?

    <p>Commodity hardware in a distributed environment</p> Signup and view all the answers

    In parallel data processing, what aspect must be managed carefully to avoid inefficiency?

    <p>The independence of sub-tasks from one another.</p> Signup and view all the answers

    How can parallel processing be achieved within a single device?

    <p>By implementing multiple threads of execution</p> Signup and view all the answers

    Which of the following correctly describes the structure of a task that can be processed in parallel?

    <p>A task that can be divided into three subordinate tasks</p> Signup and view all the answers

    What advantage does parallel processing offer in data handling?

    <p>Faster task completion times</p> Signup and view all the answers

    What is a typical method used to implement parallel processing in a computational environment?

    <p>Concurrent execution on multiple processors</p> Signup and view all the answers

    What does processing workload refer to?

    <p>The amount and nature of data processed within a specified time</p> Signup and view all the answers

    Which type of processing workload involves the continuous processing of data without interruption?

    <p>Real-time processing workload</p> Signup and view all the answers

    How does batch processing workload differ from real-time processing workload?

    <p>Batch processing delays data processing until a set time, while real-time processes data immediately.</p> Signup and view all the answers

    Which of the following is NOT a common type of processing workload?

    <p>Synthetic processing workload</p> Signup and view all the answers

    What characteristic is typically associated with interactive processing workload?

    <p>Requires immediate feedback from users</p> Signup and view all the answers

    What is a characteristic of OLAP systems?

    <p>They support fast retrieval of data without delay.</p> Signup and view all the answers

    How do OLTP systems primarily differ from OLAP systems?

    <p>OLTP systems focus on transaction processing without delay, while OLAP systems focus on data analysis.</p> Signup and view all the answers

    What is a common feature of operational systems?

    <p>They typically operate on structured data.</p> Signup and view all the answers

    In data processing with MapReduce, what is an essential advantage?

    <p>It processes large datasets in parallel across distributed systems.</p> Signup and view all the answers

    Which statement best describes the nature of data handling in OLAP systems?

    <p>OLAP systems integrate data from diverse sources for analysis.</p> Signup and view all the answers

    What are the two primary tasks involved in a MapReduce job?

    <p>Map task and reduced task</p> Signup and view all the answers

    Which statement about the structure of a MapReduce job is true?

    <p>Each job consists of a map task and a reduced task.</p> Signup and view all the answers

    How do the stages within each task in a MapReduce job operate?

    <p>They must be executed in a specific sequence.</p> Signup and view all the answers

    Which of the following best describes the relationship between tasks in MapReduce?

    <p>The reduced task cannot exist without a map task.</p> Signup and view all the answers

    What function does the reduced task serve in a MapReduce job?

    <p>To aggregate and finalize the results produced by the map task.</p> Signup and view all the answers

    Study Notes

    Big Data Concepts

    • Parallel Data Processing involves simultaneously executing multiple sub-tasks that make up a larger task. This can be done using multiple processors.
    • Distributed Data Processing is achieved by using separate, networked computers working together (a cluster). Processing tasks are divided among the physical servers in the cluster for faster processing.
    • Hadoop is an open-source framework for large-scale data storage and processing.

    Processing Workload

    • Processing workload refers to the amount and type of data processed within a specific timeframe.
    • Two types of processing workloads are:
      • Batch processing (offline processing): data is processed in large batches without immediate need for results. Queries can be complex and may involve multiple joins. Example: OLAP systems.
      • Transactional processing (online processing): data is processed instantly. Data is processed interactively without any delay. Fewer joins are typically involved, with examples including OLTP and operational systems.

    MapReduce

    • MapReduce is a widely used framework for batch processing (parallel processing). It's based on the "divide and conquer" principle.
    • It divides a large problem into smaller, easier-to-solve subproblems.
    • A single MapReduce processing run is called a MapReduce job.
    • Each MapReduce job has a map task and a reduce task, each containing multiple stages.
    • Map stage: divides the dataset into smaller splits. The mapper collects the grouped output.
    • Combine stage: a mapper's output is summarized before the reducer takes over.
    • Partition stage: The output from the combiner is divided into partitions.
    • Shuffling stage: Output from all partitioners is copied across the network to nodes running the reduce tasks
    • Sort stage: key-value pairs are sorted according to their keys.
    • Reduce stage: The reducer summarizes the input or emits the output without changing it.

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    Big Data - Session 7 (Computer Science)

    Description

    This quiz covers fundamental concepts in Big Data, including parallel and distributed data processing techniques. It also delves into processing workloads, distinguishing between batch and transactional processing. Test your understanding of Hadoop and how data processing is executed in different scenarios.

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