Chapter 8 Cloud Computing: Big Data Analytics in the Cloud

Fall 2025

What is the primary goal of analytics?

To extract meaningful insights from data.

Why does raw data lack meaning on its own?

It is not yet processed or contextualized.

Which of the following best describes analytics?

A process of transforming raw data into useful information.

What is typically done to raw data during analytics?

Filtered, processed, and categorized

In analytics, why is contextualization important?

It provides meaning by relating data to its environment

What does organizing and structuring processed information allow a system to do?

Infer knowledge and improve efficiency

What makes systems “smarter” through analytics?

Learning from processed information to guide decisions

What organization identified the seven “giants” of massive data analysis?

National Research Council (NRC)

What is the main goal of the NRC’s “seven giants” characterization?

To provide a taxonomy of key computational tasks in data analysis

Which of the following is not one of the NRC’s seven “giants”?

Data Encryption

What do the seven “giants” represent?

Core computational tasks useful for massive data analysis

Which “giant” deals with operations like regression or mean and variance calculations?

Basic Statistics

Which “giant” focuses on computations involving distances or interactions between many elements?

Generalized N-Body Problems

Which “giant” involves solving systems of equations and performing matrix operations?

Linear Algebraic Computations

Which “giant” centers on finding best or most efficient solutions under constraints?

Optimization

Which “giant” deals with relationships and connections between nodes or entities?

Graph-Theoretic Computations

What do the seven “giants” share in common?

They are grouped by mathematical structure and computational strategy.

Which type of analytics answers the question “What happened?”

Descriptive Analytics

Which type of analytics focuses on “Why did it happen?”

Diagnostic Analytics

Which type of analytics predicts “What is likely to happen?”

Predictive Analytics

Which type of analytics addresses “What can we do to make it happen?”

Prescriptive Analytics

Basic statistics (mean, median, variance) are primarily used in which type of analytics?

Descriptive Analytics

Generalized N-Body problems, like clustering and similarity, are often used in:

Diagnostic and Predictive Analytics

Linear algebraic computations (e.g., PCA, regression) are most closely tied to which analytics types?

Predictive and Diagnostic Analytics

Graph-theoretic computations, such as shortest path or centrality, are often applied in:

Predictive and Diagnostic Analytics

Optimization techniques (minimization, linear programming) are most relevant to:

Prescriptive Analytics

Integration methods like Bayesian inference and Monte Carlo simulations are mainly used for:

Predictive Analytics

Alignment problems, such as matching text or image datasets, are associated with:

Predictive and Prescriptive Analytics

Which of the following correctly matches type to example?

Predictive → Simulation

What question does Descriptive Analytics aim to answer?

What has happened?

What question does Diagnostic Analytics aim to answer?

Why did it happen?

Which type of analytics summarizes past data for easier interpretation?

Descriptive Analytics

Which type of analytics seeks to find reasons behind past events?

Diagnostic Analytics

Computing counts, means, and percentages is an example of which analytics type?

Descriptive Analytics

Identifying the cause of a machine fault by analyzing past sensor data is an example of which analytics type?

Diagnostic Analytics

Which analytics type primarily uses statistical functions like maximum, minimum, and top-N?

Descriptive Analytics

Which analytics type uses pattern recognition from historical data to explain anomalies or failures?

Diagnostic Analytics

What question does Predictive Analytics aim to answer?

What is likely to happen?

What question does Prescriptive Analytics aim to answer?

What can we do to make it happen?

Which analytics type focuses on forecasting future events or outcomes?

Predictive Analytics

Which analytics type recommends the best actions to achieve desired outcomes?

Prescriptive Analytics

Predictive models learn from existing data to forecast outcomes using which types of models?

Classification and regression models

Which analytics type uses multiple prediction models to determine the best course of action?

Prescriptive Analytics

Training a model on historical data to forecast sales next quarter is an example of which analytics type?

Predictive Analytics

Evaluating different strategies to maximize profit based on predicted outcomes is an example of which analytics type?

Prescriptive Analytics

What is Big Data primarily defined by?

Large volume, velocity, and variety of data that traditional tools cannot easily handle

Which company estimated that 2.5 quintillion bytes of data are created every day?

IBM

According to DOMO, about how many pieces of content are shared on Facebook every minute?

4.16 million

Approximately how many tweets are sent on Twitter every minute, according to DOMO?

300,000

How many photos are liked on Instagram every minute?

1.73 million

How much video content is uploaded to YouTube every minute?

300 hours

How many apps are downloaded by Apple users every minute?

51,000

How many new Skype calls are made every minute?

110,000

How many new visitors does Amazon receive every minute?

4,300

How many Uber rides are taken every minute?

694

How many hours of video are streamed by Netflix users every minute?

77,000

What does Big Data Analytics primarily deal with?

The collection, storage, processing, and analysis of massive-scale data

Which of the following is the correct sequence of steps in Big Data Analytics?

Data cleansing → Data munging (wrangling) → Data processing → Visualization

Which of the following describes data munging (or wrangling)?

Transforming and cleaning raw data into a usable format.

Why are specialized tools and frameworks required for Big Data Analytics?

Because traditional systems cannot efficiently handle large volume, high velocity, and diverse data types.

When is Big Data Analytics especially needed?

When data volume, velocity, or variety exceed the limits of single-machine processing.

What is an example of velocity in Big Data?

Data that must be analyzed in real time

Which of the following best describes variety in Big Data?

Data that can be structured, unstructured, or semi-structured from multiple sources.

What does Volume in Big Data refer to?

The extremely large amount of data that cannot fit on a single machine.

Why are specialized tools and frameworks needed for high data Volume?

To store, process, and analyze data that exceeds single-machine capacity.

What does Velocity describe in the context of Big Data?

The speed at which data is generated and arrives for processing.

Which of the following is an example of high-velocity data?

Social media posts or sensor readings generated in real time.

What does Variety refer to in Big Data?

The different forms and formats of data, such as structured, unstructured, and semi-structured.

Which of the following best represents Variety in Big Data?

Text, images, audio, video, and sensor data.

Which of the 3Vs focuses on how fast data is produced?

Velocity

Which of the 3Vs focuses on the different types of data formats?

Variety

Which of the 3Vs focuses on the scale or size of data?

Volume

What does Veracity refer to in Big Data?

The accuracy and reliability of the data

Why is data cleaning important for Veracity?

It removes noise and errors to ensure accurate analysis.

What does Value represent in Big Data?

The usefulness of the data for its intended purpose.

What is the ultimate goal of Big Data Analytics?

To extract value from the data

Which of the following ensures that the insights derived from Big Data are trustworthy?

Veracity

Which of the following ensures that data contributes meaningfully to business or research objectives?

Value

If a dataset contains duplicate or incorrect records, which “V” does it affect most?

Veracity

If data provides actionable insights that improve decision-making, which “V” does it demonstrate?

Value

What is the first step in any analytics application?

Data Collection

What must happen before data can be analyzed?

It must be collected and ingested into a big data stack.

The choice of tools for data collection depends primarily on what factors?

The source and type of data being ingested.

What is the main goal of Data Preparation?

To clean and organize data before processing.

Which of the following is a common issue addressed in data preparation?

Missing values or corrupt records.

What process removes duplicate entries from a dataset?

De-duplication

What term refers to transforming raw data into a usable format?

Data wrangling (or munging)

What does normalization in data preparation help ensure?

Consistent data formats and units across the dataset

What is the purpose of filtering during data preparation?

To remove irrelevant or unnecessary data points

What is the next step after data preparation in the analytics flow?

Determine the analysis type

Which of the following are the four main types of data analysis?

Descriptive, Diagnostic, Predictive, and Prescriptive

What comes after selecting the analysis type for an application?

Determine the analysis mode

Which of the following are common analysis modes?

Batch, Real-time, and Interactive

What does the choice of analysis mode depend on?

The requirements of the application

In which analysis mode is data processed periodically in large groups?

Batch mode

In which analysis mode is data processed instantly as it arrives?

Real-time mode

What determines the choice of visualization tools and frameworks?

The requirements of the application

Which of the following are types of data visualizations?

Static, Dynamic, and Interactive

What is the main purpose of visualizations in analytics?

To present data and insights in an understandable and meaningful way