Big Data Concepts and Workload Processing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Parallel data processing involves the simultaneous execution of multiple sub-tasks that collectively comprise a larger task.

Big data refers to massive datasets that are too large and complex to be processed by traditional methods.

A collection of subtasks that, when combined, achieve a larger, complex task.

Processing units that work together to execute subtasks in parallel, enhancing performance.

The simultaneous execution of multiple subtasks, significantly reducing the total time required to complete the main task.

A physical device containing multiple processors, enabling parallel processing by assigning subtasks to different processors within the same device.

Hadoop is an open-source software framework used for storing and processing massive datasets, distributed across multiple computers.

HDFS is a distributed file system that stores data in large blocks, spreading them across multiple nodes for efficiency and fault tolerance.

MapReduce is Hadoop's programming model for processing data. It breaks down tasks into "map" and "reduce" phases, efficiently handling large-scale computations.

Hadoop is often used in scenarios where you need to analyze massive datasets, such as in e-commerce, social media, and scientific research.

Hadoop is an open-source framework, meaning its code is freely available and can be modified by developers. This collaborative nature allows constant improvements and innovation.

The amount and type of data processed over a specific time period. It represents the 'work' that a system performs on data.

A processing workload where tasks are executed sequentially, one after the other. It's like doing dishes one by one.

A processing workload where multiple tasks are executed simultaneously, leveraging multiple processors or cores. It's like having multiple cooks working on the same meal.

A processing workload that involves executing tasks in a specific order, with each task depending on the previous one. It's like following a recipe step by step.

A processing workload where tasks can be executed in any order, without dependencies. It's like cleaning your room - you can tidy up the desk or the floor first.

A unit of work in MapReduce, responsible for processing a portion of the input data.

A unit of work in MapReduce, responsible for combining the results of multiple map tasks.

A series of smaller steps that compose a MapReduce task, transforming data from one state to another.

A program that executes the map and reduce tasks in MapReduce, allowing for parallel data processing.

The input data that is processed by the MapReduce job.

OLAP systems are designed for analytical queries involving complex calculations on large datasets. They often utilize dimensional models and cubes for efficient data retrieval and analysis.

OLTP stands for Online Transaction Processing. It's used for real-time updates and operations like banking transactions or online shopping. OLTP systems are optimized for fast and frequent updates to ensure consistent data.

MapReduce is a parallel processing framework that efficiently handles large datasets. It divides a task into independent 'map' operations for data transformation, followed by 'reduce' operations to combine results and produce the final output.

Using MapReduce for processing data involves defining 'mapper' functions to manipulate data in parallel, and 'reducer' functions to aggregate and synthesize the processed data.

In data processing, fewer joins reduce the complexity and enhance efficiency. It's important to optimize queries and data structures to minimize join operations, especially when dealing with large datasets.