Parcial 3

Question 1

Is a collection of facts, numbers, words, observations or other useful information. Through _____ processing and ____ analysis, organizations transform raw data points into valuable insights that improve decision-making and drive better business outcomes.

Answer 1

Data

Question 2

Consists of values that can be measured
numerically. Examples of this type of data include discrete data points (such as the number of products sold) or continuous data
points (such as temperature or revenue figures). Is often structured, making it easy to analyze using mathematical tools and algorithms.

Answer 2

Quantitative Data

Question 3

Is descriptive and non- numerical, capturing characteristics, concepts or experiences that numbers cannot measure. Examples include customer feedback, product reviews and social media comments. This type of data can be structured (such as coded survey responses) or unstructured (such as free-text responses or interview transcripts).

Answer 3

Qualitative Data

Question 4

Is organized in a clear, defined format, often stored in relational databases or spreadsheets. It can consist of both quantitative (such as sales figures) and qualitative data (such as categorical labels like “yes or no”)

Answer 4

Structured Data

Question 5

Lacks a strictly defined format. It often comes in complex forms such as text documents, images and videos. _______________ can include both qualitative information (such as customer comments) and quantitative elements (such as numerical values embedded in text).

Answer 5

Unstructured Data

Question 6

__________________ blends elements of structured and unstructured data. It doesn’t follow a rigid format but can include tags or markers that make it easier to organize and analyze. Examples of this type of data include XML files and JSON objects. Is widely used in
scenarios such as web scraping and data
integration projects because it offers flexibility
while retaining some structure for search and
analysis.

Answer 6

Semi-Structured Data

Question 7

Is data about data. In other words, it is information about the attributes of a data point or data set, such as file names, authors, creation dates or data types.

Answer 7

Metadata

Question 8

Refers to massive, complex data sets that traditional systems can’t handle. It includes both structured and unstructured data from sources such as sensors, social media and transactions.

Answer 8

Big Data

Question 9

Helps organizations process and analyze these large data sets to systematically extract valuable insights. It often requires advanced tools such as machine learning

Answer 9

Big Data Analytics

Question 10

The ___________ refer to five fundamental characteristics that describe the challenges of handling vast amounts of data.

Answer 10

5 Vs In Big Data

Question 11

Refers to the massive amount of data generated every second. From social media to commercial transactions, every action contributes to the __________ of Big Data.

Answer 11

First V: Volume

Question 12

Refers to the speed at which these data are generated and processed. In a world where information is power, speed is essential. Higher _________ allows companies to react in real time to emerging trends or issues, which can provide a significant competitive advantage.

Answer 12

Second V: Velocity

Question 13

For instance, financial trading platforms process millions of transactions per second, requiring high-speed data processing. Also, sensors in autonomous vehicles generate gigabytes of data per second that must be processed in real time to make navigation decisions.

Answer 13

Examples of “Velocity”

Question 14

A social network like Facebook generates terabytes of data every day through photos, status updates, and messages that users share. Imagine the volume of data that entails. On the other hand, Walmart, one of the largest retail chains, handles more than 1 million customer transactions per hour, which translates into large volumes of data.

Answer 14

Examples of “Volume”

Question 15

Refers to the different types of data, such as structured, unstructured, and semi-structured, that can be processed and analyzed. This V allows a more comprehensive and enriching understanding of the environment by considering multiple perspectives and sources of information.

Answer 15

Third V: Variety

Question 16

For a practical example, we could say that companies can collect data from various sources such as texts, images, sounds, transaction logs, emails, etc. Also, a hospital may have structured data like medical records, and unstructured data like doctors’ notes and medical imaging results.

Answer 16

Examples of “Variety”

Question 17

Refers to the quality and accuracy of the data. The data must be accurate and reliable to obtain valid insights. It’s essential for making informed decisions and avoiding erroneous conclusions that can be costly.

Answer 17

Fourth V: Veracity

Question 18

Veracity can be a challenge on social media, where information can be incorrect or misleading. In the medical field, incorrect or incomplete data can have severe consequences, making it crucial to ensure the veracity of the data.

Answer 18

Examples of “Veracity”

Question 19

Refers to the usefulness and importance of the data and how they can be used to gain benefits and insights. The ______ of the data lies in how they can be used to improve decision-making, optimize processes, and generate new opportunities.

Answer 19

Fifth V: Value

Question 20

Giants like Netflix or Amazon assign superior utility to data. Netflix, for example, uses Big Data to analyze user preferences and recommend movies and series, creating value through a better user experience. On the other hand, Amazon uses Big Data analytics to optimize its logistics and supply chain, resulting in faster delivery and better customer service.

Answer 20

Examples of “Value”

Question 21

Data enables organizations to transform raw information into actionable insights to predict customer behavior, optimize supply chains and fuel innovation. The term “data” comes from the plural of “datum”, a Latin word meaning “something given”: a definition that remains just as relevant today. Every day, millions of people provide data to businesses through interactions such as impressions, clicks, transactions, sensor readings or even just browsing online.

Answer 21

Why Data Is Important

Question 22

Organizations across industries use data for various purposes, including improving decision-making, streamlining operations and driving innovation.

Answer 22

How Data Is Used

Question 23

Is a branch of advanced analytics that predicts future trends and outcomes using historical data combined with statistical modeling, data mining and machine learning.

Answer 23

Predictive Analytics

Question 24

Sometimes called gen AI, is artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. ___________ relies on sophisticated machine learning models called deep learning models. These models are trained on vast data sets, which allows them to do things such as understand users’ requests, generate personalized marketing content and write code.

Answer 24

Generative AI

Question 25

Data analytics can help healthcare providers improve patient care, predict disease outbreaks and enhance treatment protocols. For instance, monitoring patients through time series data, such as tracking patient vitals over time, provides real-time insights into patient conditions.

Answer 25

Healthcare Innovations

Question 26

Frequently analyze quantitative and qualitative data from surveys, census reports and social media. Examining these data sets allows them to study behaviors, trends and policy impacts. For instance, researchers might use census data to track population changes, survey responses to measure public opinion and social media data to analyze emerging trends.

Answer 26

Social Science Research

Question 27

As cyberattacks and data breaches become more frequent, organizations are increasingly turning to data analysis to identify and respond to threats faster, minimizing damage and reducing downtime.

Answer 27

Cibersecurity and Risk Management

Question 28

Machine learning algorithms, trained on vast data sets, can help organizations boost operational efficiency by optimizing logistics, predicting demand, improving scheduling and automating workflows. For example, e-commerce companies frequently collect and analyze real-time sales data to inform inventory management, reducing the likelihood of stockouts or overstocking.

Answer 28

Operational Efficiency

Question 29

Data is the backbone of personalized customer experiences, particularly in marketing, where organizations can use data analytics to tailor content and ads to different users. For example, streaming services rely on machine learning algorithms to analyze viewing habits and recommend content.

Answer 29

Customer Experience

Question 30

Governments worldwide frequently use open data policies to make valuable data sets publicly accessible, encouraging businesses and organizations to use these resources for research and innovation. For example, the US government's Data.gov platform provides access to various data sets across healthcare, education and transportation.

Answer 30

Government Initiatives

Question 31

Is a set of technological processes for collecting, managing and analyzing data, turning raw data into insights that can guide business decisions.

Answer 31

Business Intelligence (BI)

Question 32

Complements BI by helping organizations interpret and visualize data through graphs, dashboards and reports, making it easier to spot trends and make informed decisions.

Answer 32

Business analytics

Question 33

Is the systematic process of gathering data from various sources while helping to ensure its quality and integrity. Typically performed by data scientists and analysts, it is the foundation for accurate and reliable data analysis. __________________ starts with setting clear objectives and identifying relevant sources. Data is then acquired, cleaned and integrated into a unified data set.

Answer 33

Data collection

Question 34

Data storage systems and ongoing quality checks help ensure the collected data is...

Answer 34

accurate and reliable

Question 35

Organizations handle vast amounts of data in multiple formats scattered across public and private clouds, making data fragmentation and mismanagement significant challenges. This is the practice of collecting, processing and using data securely and efficiently to improve business outcomes. It addresses critical challenges such as managing large data sets, breaking down silos and handling inconsistent data formats.

Answer 35

Data management

Question 36

Are low-cost storage environments that house raw, unstructured data, which can later be processed and analyzed.

Answer 36

Data lakes

Question 37

Store structured data from various sources, optimized for data mining and analysis tasks

Answer 37

Data warehouses

Question 38

Merge the best aspects of data warehouses and data lakes, offering a unified solution for managing both structured and unstructured data.

Answer 38

Data lakehouses

Question 39

Data management solutions typically integrate with existing infrastructure to help ensure access to high-quality, usable data for data scientists, analysts and other stakeholders. These solutions often incorporate data lakes, data warehouses or data lakehouses, combined in a unified __________.

Answer 39

Data fabric

Question 40

Perform complex, foundational data tasks. For example, they create models and algorithms to find insights in large data sets, often using advanced tools such as machine learning and predictive modeling.

Answer 40

Data scientist

Question 41

Focus on more immediate, practical tasks. They use statistics to analyze data and answer specific business questions. Their main goal is to find useful insights that help with everyday decisions and strategies.

Answer 41

Data analyst

Question 42

Is the practice of safeguarding sensitive information from data loss, theft and corruption. ____________ is increasingly important as organizations handle larger volumes of sensitive data across complex, distributed environments.

Answer 42

Data Protection

Question 43

Involves protecting digital information from unauthorized access, corruption or theft. It encompasses various aspects of information security, spanning physical security, organizational policies and access controls.

Answer 43

Data Security

Question 44

Focuses on policies that support the general principle that a person should have control over their personal data, including the ability to decide how organizations collect, store and use their data.

Answer 44

Data Privacy

Question 45

Employees or contractors with authorized access can pose significant risks. According to the Cost of a Data Breach Report, data breaches initiated by malicious insiders cost USD 4.99 million on average.

Answer 45

Insider Threads

Question 46

Threat actors often use __________________ attacks such as phishing to exploit human weaknesses to trick individuals into revealing sensitive information. Generative AI tools can now craft highly convincing phishing emails, increasing the success rate of such attacks.

Answer 46

Social Engineering

Question 47

Cybercriminals use __________ to encrypt an organization’s data and demand a ransom in exchange for the decryption key. Healthcare systems, financial institutions and government data agencies are particularly vulnerable to these attacks.

Answer 47

Ransomware

Question 48

With the widespread adoption of cloud services, misconfigurations, insecure APIs and poor access control can lead to public data leaks. According to the Cost of a Data Breach Report, data breaches involving public clouds are the most expensive, costing USD 5.17 million on average.

Answer 48

Cloud Security

Question 49

Organizations use various data protection technologies to defend against threat actors and help ensure data integrity, confidentiality and availability.

Answer 49

Data Protection Solutions

Question 50

Uses symmetric encryption or asymmetric encryption to protect data during storage and transmission, preventing attackers from reading or misusing it. End-to-end encryption (E2EE) specifically encrypts data before transferring it to another endpoint, keeping it secure throughout its journey.

Answer 50

Encryption

Question 51

Regularly create and store copies of critical data, allowing fast restoration if there is loss or corruption while minimizing downtime.

Answer 51

Data backups

Question 52

Monitor and control network traffic, acting as the first line of defense to block unauthorized access.

Answer 52

Firewalls

Question 53

Verify user identities and control access to sensitive information. Multi-factor authentication (MFA) adds an extra layer of security, requiring users to provide multiple forms of verification.

Answer 53

Authentication and authorization

Question 54

Manages how users access digital resources and what they can do with those resources to reduce insider threats and prevent unauthorized access.

Answer 54

Identity and access management (IAM)

Question 55

Detect, prevent and remove malicious software such as viruses, spyware and ransomware that could compromise data.

Answer 55

Antivirus and anti-malware tools

Question 56

Monitor user activity and flag suspicious behavior to prevent unauthorized access, transmission or leakage of sensitive information.

Answer 56

Data loss prevention (DLP) tools

Question 57

Real-time data from platforms such as Twitter and Facebook can be used to track brand engagement, gauge public opinion and discover consumer sentiment.

Answer 57

Data sources: Social Media Interactions

Question 58

Freely available data sets from governments and organizations, such as census data and economic indicators, can help provide context for demographic shifts, market segmentation and financial analysis

Answer 58

Data sources: Public data

Question 59

Data sets from academic institutions and governments on topics such as climate change and geospatial data are often used for research and policymaking.

Answer 59

Data sources: Open data sets

Question 60

Data from business transactions, such as sales records, invoices and payment information, can help businesses track performance, optimize pricing and improve the customer experience.

Answer 60

Data sources: Transactional data

Question 61

Qualitative or quantitative data collected through customer feedback or research surveys can provide insights into preferences, opinions and trends.

Answer 61

Data sources: Surveys and questionnaires

Question 62

Data from website interactions, such as page views and click-through rates, help companies understand user behavior, optimize content and improve user experiences.

Answer 62

Data sources: Web analytics

Question 63

Data from Internet of Things (IoT) devices such as smart meters and wearable trackers can support real-time analytics and predictive maintenance and prevent equipment downtime.

Answer 63

Data sources: IoT devices

Question 64

Is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction

Answer 64

Cloud Computing

Question 65

Are delivered as services where a cloud is called a public cloud when it is made available in a pay-as-you-go manner to the general public, and is called a private cloud when the cloud infrastructure is operated solely for a business or an organization

Answer 65

Cloud Computing Resources

Question 66

-> Cloud Services + Applications are accessible from several client devices + The provider is responsible for the application + Examples, SalesForce.com, NetSuit, Google, IBM, etc.

Answer 66

Software-as-a-Service (SaaS)

Question 67

-> Cloud Services + The client is responsible for the end-to-end life cycle in terms of developing, testing and deploying applications + Providers supplies all the systems (operating systems, applications, and development environment) + Examples are Google’s appEngine, Microsoft´s Azure, etc.

Answer 67

Platform-as-a-Service (PaaS)

Question 68

-> Cloud Services + The service client has control over the operating system, storage, and applications which are offered through a Web-based access point + In this type of service the client manages the storing and development environments for Cloud Computing application such as the Hadoop Distributed File System (HDFS) and the MapReduce development framework. + Examples of infrastructure providers are GoGird, AppNexeus, Eucalyptus, Amazon EC2, etc

Answer 68

Infrastructure-as-a-Service (IaaS)

Question 69

Cloud Computing (CC) is a technology aimed at processing and storing very large amounts of data also known as

Answer 69

Big Data (BD)

Question 70

Refers to an eclectic and increasingly familiar group of nonrelational data management systems; where databases are not built primarily on tables, and generally do not use SQL for data manipulation. This systems are distributed, non-relational databases designed for large-scale data storage and for massively parallel data processing across a large number of commodity servers.

Answer 70

NoSQL (Not Only SQL)

Question 71

Organizations that collect large amounts of unstructured data are increasingly turning to non-relational databases, now frequently called ____________ . This focus on analytical processing of large scale datasets, offering increased scalability over commodity hardware.

Answer 71

NoSQL databases

Question 72

1. The exponential growth of the volume of data generated by users, systems and sensors, further accelerated by the concentration of large part of this volume on big distributed systems like Amazon, Google and other cloud services. 2. The increasing interdependency and complexity of data accelerated by the Internet, Web2.0, social networks and open and standardized access to data sources from a large number of different systems.

Answer 72

Two trends to use NoSQL databases

Question 73

Conflicts are arising between the different aspects of high availability in distributed systems that are not fully solvable - known as the _________

Answer 73

CAP-theorem

Question 74

CAP-theorem ----------- "All clients see the same version of the data, even on updates to the dataset."

Answer 74

Strong Consistency

Question 75

CAP-theorem ----------- "All clients can always find at least one copy of the requested data, even if some of the machines in a cluster is down."

Answer 75

High Availability

Question 76

"The total system keeps its characteristic even when being deployed on different servers, transparent to the client."

Answer 76

Partition-tolerance

Question 77

1. Large-scale data processing (parallel processing over distributed systems). 2. Embedded I-R (basic machine-to-machine information look-up & retrieval). 3. Exploratory analytics on semi-structured data (expert level). 4. Large volume data storage (unstructured, semi-structured, small-packet structured).

Answer 77

Primary uses of NoSQL Database

Question 78

Typically, these Data Management Systems (DMS) store items as alphanumeric identifiers (keys) and associated values in simple, standalone tables (referred to as ―hash tables‖). The values may be simple text strings or more complex lists and sets.

Answer 78

Key-Value stores

Question 79

Are designed to manage and store documents which are encoded in a standard data exchange format such as XML, JSON (Javascript Option Notation) or BSON (Binary JSON). Unlike the simple key-value stores described above, the value column in document databases contains semi-structured data – specifically attribute name/value pairs. Unlike simple key-value stores, both keys and values are fully searchable in document databases.

Answer 79

Document databases

Question 80

This type of NoSQL Database employs a distributed, column-oriented data structure that accommodates multiple attributes per key. These NoSQL Databases generally replicate not just Google‘s Bigtable data storage structure, but Google‘s distributed file system (GFS) and MapReduce parallel processing framework as well, as is the case with Hadoop, which comprises the Hadoop File System (HDFS, based on GFS) + Hbase (a Bigtable-style storage system) + MapReduce.

Answer 80

Wide-Column (or Column-Family)

Question 81

___________________ replace relational tables with structured relational graphs of interconnected key-value pairings. They are similar to object oriented databases as the graphs are represented as an object-oriented network of nodes (conceptual objects), node relationships (―edges‖) and properties (object attributes expressed as keyvalue pairs). In general, _________________ are useful when you are more interested in relationships between data than in the data itself.

Answer 81

Graph Databases

Question 82

What is HBase?

Answer 82

Column-oriented database management system that runs on top of Hadoop Distributed File System. Is an open-source implementation of the Google BigTable architecture.

Question 83

HBAse provides consistent read and write operations and thus can be used for high speed requirements. This also helps to increase the overall throughput of the system.

Answer 83

Consistency

Question 84

Means that only one process can perform a given task at a given time. For example when one process is performing write operation no other processes can perform the write operation on that data.

Answer 84

Atomic Read and Write

Question 85

HBase offers automatic and manual splitting of regions. This means that if a region reaches its threshold size it automatially splits into smaller sub regions.

Answer 85

Sharding

Question 86

HBase provides Local Area Network(LAN) and Wireless Area Network(WAN) which supports failure recovery. There is a master server which monitors all the regions and metadata of the cluster.

Answer 86

High Availability

Question 87

HBase offers access through the Java API which helps to programmatically access HBase.

Answer 87

Client API

Question 88

This is one of the important characteristics of non-relational databases. HBase supports _____________ both in linear and modular form.

Answer 88

Scalability

Question 89

This feature of HBase helps usage of distributed storage such as HDFS. And HBase can run on top of various systems such as Hadoop/HDFS

Answer 89

Distributed Storage and HDFS/Hadoop integration

Question 90

The data in HBase are replicated over a number of clusters. This helps to recover data in case of any loss and high availability of data.

Answer 90

Data Replication

Question 91

HDFS is internally distributed and supports automatic recovery. As HBase runs on top of HDFS it is also automatically recovered.

Answer 91

Load sharing and Support for Failure

Question 92

HBase supports Java API which makes it easily available programmatically using java. And HBase supports map reduce which helps in parallel processing of data.

Answer 92

API and MapReduce Support

Question 93

HBase uses keys and stores it in lexicographical order thus optimizing the requests.

Answer 93

Sorted Row Keys

Question 94

HBase performs real time processing of data and supports block cache and bloom filters.

Answer 94

Real Time Processing of Data

Question 95

When we are working with Big Databases, it is often a requirement that we design schema differently. Terms to describe the above principle are ________, __________ and ______________. It is about rethinking how data is stored in Bigtable-like storage systems, and how to make use of it in an appropriate way

Answer 95

Denormalization, Duplication, and Intelligent Keys (DDI).