When determining data and information asset handling, regardless of the specific toolset being used, which of the following is one of the common components of big data?
When determining data and information asset handling, regardless of the specific toolset being used, which of the following is one of the common components of big data?
Distributed storage locations are a common component of big data. Big data involves extremely large and complex datasets that cannot be stored or processed on a single machine or in a single location. Instead, big data is typically distributed across a large number of storage nodes or servers, with each node storing a portion of the data. This distribution allows for scalability, increased performance, and fault tolerance when dealing with large amounts of data.
A is considered the better answer than C because distributed storage locations are a common component of big data because big data often involves extremely large and complex datasets that cannot be stored or processed on a single machine or in a single location. Instead, big data is typically distributed across a large number of storage nodes or servers, with each node storing a portion of the data. This allows for scalability, increased performance and fault tolerance when dealing with large amounts of data. On the other hand, distributed data collection is a process in which data is collected from multiple sources, often in different locations, and then brought together for processing and analysis. This approach can be important in some big data scenarios, but it is not a fundamental requirement of big data as a whole. Therefore, A is more aligned with the big data concept.
From the CISSP Office Study Guide - "Data warehouse technologies allow organizations to integrate data from databases, warehouses, lakes, or other environments from across the internet Collectively, this is all loosely referred to as big data.'
A. Distributed storage locations - C Realize that Big Data server clusters will not be a single physical entity but even thousands of individual computer nodes connected together
100% correct!
Distributed data collection is one of the common components of big data, as it refers to the process of gathering and ingesting data from various sources, such as sensors, social media, web logs, transactions, etc. Distributed storage locations is not a component of big data, but rather a characteristic of big data storage systems that store the data across multiple nodes or clusters for fault tolerance and performance.
big data = data lake. answer is fine.
distributed means spread out. wouldn't a data lake be consolidated?
Data will be split and uploaded for distributed cloud storage system. Single split data will be worthless until and unless joined with the other parts of the data. There would be a security concern of data location.
Destination CISSP says that the first attribute of big data is Variety, which means data cab be pulled from different sources in different format. I believe that distributed data collection is.
Data storage. Data for batch processing operations is typically stored in a distributed file store that can hold high volumes of large files in various formats. This kind of store is often called a data lake.
A makes total sense but checking on the web the C also seems to be right: https://www.javatpoint.com/big-data-characteristics
Distributed storage in multiple Cloud locations, geographic regions, and zones, is essential for fault tolerance and compliance.
I go with A
Given answer is correct. Big data consolidates Edge and Fog layers over distributed data collections (sensor).
A. Distributed storage locations
A is correct
This refers to the practice of storing large volumes of data across multiple locations or nodes, often using technologies such as Hadoop Distributed File System (HDFS).
Definetly, A. Distributed storage locations.
Data warehouse technologies allow organizations to integrate data from databases, warehouses, lakes or other environments from across the internet. Collectively, this is all loosely referred to as big data.
Going with C on this one = Distributed data collection
Distributed storage & distributed processing are core to big data. Distributed data collection is beneficial but not mandatory—big data systems can still function with centralized data collection, provided they efficiently handle storage and processing. It requires storage across multiple nodes instead of a single centralized location. Large datasets are stored across a cluster of machines, allowing parallel processing.
Big data, by its nature, involves massive volumes of data. Storing this data in a single, centralized location is often impractical and inefficient. Distributed storage across multiple servers or locations allows for scalability, fault tolerance, and efficient data access.