D495 Big Data Foundations

Explanation:

Moore's Law predicts that the number of transistors on a microchip doubles approximately every two years, which leads to exponential growth in computing power and a decrease in cost per transistor. This principle is significant because it has historically driven rapid advancements in computer hardware, enabling faster, smaller, and more energy-efficient devices. Moore’s Law has guided hardware development, influenced software design, and shaped expectations for technological innovation, making it a fundamental concept in understanding the evolution of computing performance.

Correct Answer:

Moore's Law predicts that the number of transistors on a microchip doubles approximately every two years, driving rapid advancements in computer hardware.

Why Other Options Are Wrong:

Moore's Law indicates that the size of computer memory decreases over time.

This option is incorrect because Moore’s Law is about the doubling of transistors on a microchip and the resulting increase in processing power, not specifically about memory size. While memory may improve as a consequence, this is not the law’s focus.

Moore's Law suggests that computer software development should focus on parallel programming.

This option is incorrect because Moore's Law addresses hardware capabilities, not software development approaches. Parallel programming is a technique used to optimize software performance but is not directly implied by Moore’s Law.

Moore's Law states that computer performance doubles every year.

This option is incorrect because the original observation by Gordon Moore was that transistor density doubles approximately every two years, not every year. Stating one year is an inaccurate representation of Moore’s Law.

Explanation:

Flume is a distributed, reliable, and available service designed specifically for collecting, aggregating, and moving large volumes of streaming data into HDFS in real-time. It is commonly used for log data and event data ingestion from multiple sources into Hadoop for storage and analysis. Kafka, while also handling real-time streaming, acts as a messaging system rather than a direct ingestion tool into HDFS. Sqoop is designed for bulk import/export between relational databases and Hadoop, and Hive is a data warehouse tool for querying and managing data within HDFS.

Correct Answer:

Flume

Why Other Options Are Wrong:

Kafka

This is incorrect because Kafka is a distributed messaging system that handles real-time data streams but does not directly ingest data into HDFS. It requires additional integration for storage.

Sqoop

This is incorrect because Sqoop is used for transferring data between relational databases and Hadoop, primarily for batch operations, not real-time streaming ingestion.

Hive

This is incorrect because Hive is a data warehouse framework for querying and analyzing data stored in HDFS. It does not handle data ingestion from streaming sources.

Explanation:

The characteristics of Big Data, including high volume, high variety, and high velocity, significantly impact how data is analyzed and used in decision-making. Large and diverse datasets require advanced analytical tools, such as machine learning algorithms, distributed computing platforms, and real-time processing systems, to derive meaningful insights. Decision-makers rely on these insights to guide strategy, operations, and policy. Without appropriate analytical capabilities, the vast scale and complexity of Big Data can overwhelm traditional systems, leading to missed opportunities and suboptimal decisions.

Correct Answer:

The characteristics of Big Data, such as high volume and variety, require advanced analytical tools and techniques to extract meaningful insights for decision-making.

Why Other Options Are Wrong:

Big Data's characteristics make it easy to analyze and interpret without specialized tools.

This option is incorrect because the scale and complexity of Big Data make traditional tools insufficient. Specialized software and techniques are necessary to handle large, diverse, and fast-moving datasets effectively.

High velocity and value of Big Data simplify the decision-making process.

This option is incorrect because high velocity and high value actually increase the need for rapid, accurate processing and sophisticated analysis. They do not inherently simplify decision-making without the right tools.

The characteristics of Big Data do not significantly impact its analysis or usage in decision-making.

This option is incorrect because Big Data’s characteristics have a major impact on analysis. Ignoring these factors would result in inefficient processing, incomplete insights, and poor-quality decisions.

Explanation:

In MapReduce, the default number of reducers is 1 if the programmer does not specify otherwise. The number of mappers is determined by the number of input splits, which is based on the input data size and HDFS block size, and is not fixed to 5. Hadoop allows programmers to configure the number of reducers explicitly, while the number of mappers is generally decided automatically based on the input splits. This design allows flexibility in processing large datasets efficiently.

Correct Answer:

Default number of reducers is 1

Why Other Options Are Wrong:

It can create only 5 Mappers no more than that

This option is incorrect because the number of mappers depends on the input split size and HDFS block size, not a fixed number like 5.

It creates only 5 Splits, no more or no less

This option is incorrect because the number of splits is dynamic and depends on the total input data and the configured split size, not a fixed value.

The programmer can specify neither the number of mappers nor the number of reducers. The Hadoop framework does that automatically

This option is incorrect because while the framework automatically determines the number of mappers, the programmer can explicitly specify the number of reducers if desired.

Explanation:

Big Data is characterized by the three primary dimensions known as the three Vs: Volume, Variety, and Velocity. Volume refers to the large amounts of data generated, Variety refers to the different types and formats of data, and Velocity refers to the speed at which data is created and processed. Collectively, these three aspects define the nature and challenges of Big Data, making it distinct from smaller, more traditional datasets.

Correct Answer:

All of the above

Why Other Options Are Wrong:

volume

This is incorrect because volume alone does not define Big Data. While the amount of data is important, Big Data also involves the variety of formats and the velocity of data generation.

variety

This is incorrect because variety alone is insufficient to describe Big Data. Data diversity is a key feature, but without considering volume and velocity, the term Big Data is incomplete.

velocity

This is incorrect because velocity alone does not capture the full scope of Big Data. Speed is important, but Big Data is also defined by its large volume and diverse types of data.

Explanation:

Diffusion of Innovation refers to the process by which new products, services, or ideas are communicated and spread among members of a society or social system over time. This theory, developed by Everett Rogers, explains how, why, and at what rate innovations are adopted, highlighting the roles of innovators, early adopters, and the majority. Understanding diffusion helps organizations plan marketing strategies, adoption campaigns, and educational initiatives to accelerate acceptance and usage of innovations.

Correct Answer:

the process whereby new products, services and ideas are distributed to members of society.

Why Other Options Are Wrong:

the process whereby information is distributed throughout an organization.

This option is incorrect because diffusion of innovation focuses on spreading new ideas or products in society at large, not internal information flow within a single organization.

the process whereby technology is adapted for consumer use.

This option is incorrect because adaptation is a step within the diffusion process but does not capture the overall concept, which encompasses the distribution and adoption of innovations broadly.

the process whereby innovation is adopted by corporations.

This option is incorrect because diffusion of innovation applies to society as a whole, including individuals and organizations, not solely corporations.

Explanation:

HBase is a distributed, scalable, NoSQL database built on top of the Hadoop ecosystem. It is designed to store and manage large amounts of sparse data across a cluster of commodity servers. HBase provides real-time read/write access to Big Data and supports flexible schema design, making it suitable for handling unstructured and semi-structured data. Unlike traditional SQL databases, HBase does not require a fixed schema and is optimized for large-scale data storage and retrieval.

Correct Answer:

NoSQL database

Why Other Options Are Wrong:

Physical layer of Hadoop

This option is incorrect because HBase is not a physical storage layer; it is a database application that runs on top of Hadoop, typically using HDFS for storage.

None of the mentioned

This option is incorrect because HBase clearly fits the category of a NoSQL database.

SQL database

This option is incorrect because HBase does not use SQL for data storage or querying in the traditional relational database sense. It uses its own API and supports queries via HBase Shell or integration with tools like Apache Phoenix.

Explanation:

Moore’s Law predicts that the number of transistors on an integrated circuit doubles approximately every two years, which indirectly increases computing power and storage capacity. As a result, technological capabilities expand rapidly, enabling larger and more complex datasets to be stored and processed efficiently. This ongoing growth drives innovation in computing, data storage, and overall technology advancement, making it possible for organizations to handle Big Data more effectively over time.

Correct Answer:

Moore's Law implies that as the power of computers doubles, information storage capacity also increases, leading to rapid advancements in technology.

Why Other Options Are Wrong:

Moore's Law has no impact on information storage capacity.

This is incorrect because Moore’s Law directly affects computing and indirectly enables higher storage capacities. Ignoring this relationship overlooks its significance in technology advancement.

Moore's Law suggests that technology will stagnate due to limited storage capacity.

This is incorrect because Moore’s Law predicts growth and improvement, not stagnation. It has historically driven increases in processing and storage capabilities.

Moore's Law indicates that storage capacity decreases as technology advances.

This is incorrect because Moore’s Law leads to expansion, not reduction, of storage and computing power. Storage capacity increases as technology improves, contrary to this option.

Explanation:

The primary challenge associated with data volume in Big Data is handling the sheer scale of data generated, which requires substantial storage capacity, processing power, and analytical capability. Large datasets can overwhelm traditional systems, making it difficult to efficiently store, manage, and extract actionable insights. Addressing these challenges often involves distributed storage systems, parallel processing, and specialized Big Data tools to ensure scalability and performance.

Correct Answer:

Storing, processing, and analyzing massive datasets

Why Other Options Are Wrong:

Data variety

This is incorrect because data variety refers to the diversity of data formats and sources, not the scale or volume of data. While variety presents its own challenges, it is distinct from volume.

Data governance

This is incorrect because data governance involves policies and practices for data quality, security, and compliance. It is an important aspect of Big Data management but does not directly address the challenges caused by large volumes of data.

Data velocity

This is incorrect because velocity refers to the speed at which data is generated and processed. While important, it is a separate dimension from the challenges associated with volume.