C724 Study Guide - Chapter 6: Data Resource Management
Chapter 6-1: What are the problems of managing data resources in a traditional file
environment?
Q: List and describe each of the components in the data hierarchy.
- 1. Bit: Smallest unit of data (0 or 1).
- 2. Byte: A group of 8 bits representing a single character.
- 3. Field: A group of bytes; represents a data attribute (e.g., last name).
- 4. Record: A group of related fields (e.g., all fields about one customer).
- 5. File: A collection of related records.
- 6. Database: A collection of related files organized to serve many applications efficiently.
Q: Define and explain the significance of entities, attributes, and key fields.
- Entity: A person, place, thing, or event about which data is stored (e.g., a student).
- Attribute: A characteristic or quality of an entity (e.g., student ID, name).
- Key Field: A unique identifier for each record (e.g., student ID). Key fields enable efficient
record retrieval and relationship linking.
Q: List and describe the problems of the traditional file environment.
- 1. Data Redundancy: Same data stored in multiple places, leading to wasted space.
- 2. Data Inconsistency: Updates in one place may not be reflected elsewhere, causing
discrepancies.
- 3. Lack of Data Integration: Data spread across different systems, hard to consolidate.
- 4. Data Dependence: Application and data are tightly coupled, any change to data format
requires software changes.
- 5. Limited Data Sharing and Accessibility: Data not easily accessible or shareable across
departments.
- 6. Poor Security: Difficult to control access and ensure data integrity across multiple files.
,Chapter 6-2: What are the major capabilities of database management systems (DBMS), and
why is a relational DBMS so powerful?
Q: Define a database and a database management system.
- Database: A collection of data organized to service many applications at the same time by
storing and managing data so that they appear to be in one location.
- DBMS: Special software to create and maintain a database and enable individual business
applications to extract the data they need without having to create separate files or data
definitions in their computer programs
Q: Name and briefly describe the capabilities of a DBMS.
1. Data Definition Language (DDL):
Used to define the database structure, such as creating tables and specifying field
types.
2. Data Dictionary:
Stores metadata—details about each data element (name, type, size, usage,
ownership, security, etc.).
3. Data Manipulation Language (DML):
Allows users to add, modify, delete, and retrieve data. The most common DML is
SQL (Structured Query Language).
4. Querying and Reporting:
Enables users to run queries and generate reports to answer data-related questions
or support decision-making.
Q: Define a relational DBMS and explain how it organizes data.
A relational DBMS organizes data into two-dimensional tables (called relations), where
each table represents an entity and its attributes.
How it organizes data:
Tables (Relations): Represent entities (e.g., SUPPLIER, PART).
Rows (Records): Hold individual instances of an entity.
Columns (Fields): Represent attributes (e.g., name, ID).
Primary Key: Uniquely identifies each row in a table.
Foreign Key: Links records across different tables.
.
,Q: List and describe the three operations of a relational DBMS.
- 1. Select: Retrieves rows that meet certain criteria.
- 2. Join: Combines data from two or more tables using related fields.
- 3. Project: Extracts specific columns from a table.
Q: Explain why nonrelational databases are useful.
- - Handle large volumes of unstructured/semi-structured data (e.g., JSON, XML).
- - Scale horizontally for performance and flexibility (NoSQL).
- - Useful for real-time analytics, social media, and IoT data.
Q: Define and describe normalization and referential integrity and explain how they
contribute to a well-designed relational database.
- Normalization: The process of creating small stable data structures from complex groups
of data when designing a relational database.
- Referential Integrity: Ensures relationships between tables remain consistent using
foreign keys.
- Benefit: Supports data accuracy, avoids anomalies, and enhances database efficiency.
Q: Define and describe an entity-relationship diagram and explain its role in database
design.
- Entity-Relationship Diagram (ERD): A methodology for documenting databases illustrating
the relationship between various entities in the database
- Role: Helps design and structure databases logically before implementation, ensuring
completeness and accuracy.
Chapter 6-3: What are the principal tools and technologies for accessing information from
databases to improve business performance and decision making?
Q: Define big data and describe the technologies for managing and analyzing it.
- Big Data: Data sets with volumes so huge that they are beyond the ability of typical
relational DBMS to capture, store, and analyze. The data are often unstructured or semi
structured.
- Technologies: Include Hadoop, Spark, NoSQL databases, and cloud data platforms for
distributed storage and analysis.
, Q: List and describe the components of a contemporary business intelligence technology
infrastructure.
Contemporary business intelligence (BI) technology infrastructure refers to the
modern tools and systems used to collect, store, process, and analyze large and diverse data
from multiple sources to support better decision-making.
1. Data Warehouses & Data Marts:
Store structured current and historical data for reporting and analysis; data marts serve
specific departments.
2. Hadoop:
Processes large volumes of structured and unstructured data across distributed systems;
includes HDFS and MapReduce.
3. In-Memory Computing:
Uses RAM for fast data access and real-time analytics (e.g., SAP HANA, Oracle In-Memory).
4. Analytic Platforms:
High-speed systems optimized for large-scale data analysis (e.g., IBM PureData, NoSQL
tools).
5. Data Lakes:
Store raw, unstructured/structured data in native format for future analysis, often using
Hadoop.
Q: Describe the capabilities of online analytical processing (OLAP).
- OLAP: Capability for manipulating and analyzing large volumes of data from multiple
perspectives.
- 1. Multidimensional Analysis:
View data across multiple dimensions like product, region, and time.
2. Flexible Data Views:
“Slice and dice” to explore data from different perspectives.
3. Ad Hoc Querying:
Quickly answer spontaneous, complex business questions.
4.Comparative Analysis:
Compare actual vs. projected, or current vs. historical performance.
5. Data Cube Visualization:
Chapter 6-1: What are the problems of managing data resources in a traditional file
environment?
Q: List and describe each of the components in the data hierarchy.
- 1. Bit: Smallest unit of data (0 or 1).
- 2. Byte: A group of 8 bits representing a single character.
- 3. Field: A group of bytes; represents a data attribute (e.g., last name).
- 4. Record: A group of related fields (e.g., all fields about one customer).
- 5. File: A collection of related records.
- 6. Database: A collection of related files organized to serve many applications efficiently.
Q: Define and explain the significance of entities, attributes, and key fields.
- Entity: A person, place, thing, or event about which data is stored (e.g., a student).
- Attribute: A characteristic or quality of an entity (e.g., student ID, name).
- Key Field: A unique identifier for each record (e.g., student ID). Key fields enable efficient
record retrieval and relationship linking.
Q: List and describe the problems of the traditional file environment.
- 1. Data Redundancy: Same data stored in multiple places, leading to wasted space.
- 2. Data Inconsistency: Updates in one place may not be reflected elsewhere, causing
discrepancies.
- 3. Lack of Data Integration: Data spread across different systems, hard to consolidate.
- 4. Data Dependence: Application and data are tightly coupled, any change to data format
requires software changes.
- 5. Limited Data Sharing and Accessibility: Data not easily accessible or shareable across
departments.
- 6. Poor Security: Difficult to control access and ensure data integrity across multiple files.
,Chapter 6-2: What are the major capabilities of database management systems (DBMS), and
why is a relational DBMS so powerful?
Q: Define a database and a database management system.
- Database: A collection of data organized to service many applications at the same time by
storing and managing data so that they appear to be in one location.
- DBMS: Special software to create and maintain a database and enable individual business
applications to extract the data they need without having to create separate files or data
definitions in their computer programs
Q: Name and briefly describe the capabilities of a DBMS.
1. Data Definition Language (DDL):
Used to define the database structure, such as creating tables and specifying field
types.
2. Data Dictionary:
Stores metadata—details about each data element (name, type, size, usage,
ownership, security, etc.).
3. Data Manipulation Language (DML):
Allows users to add, modify, delete, and retrieve data. The most common DML is
SQL (Structured Query Language).
4. Querying and Reporting:
Enables users to run queries and generate reports to answer data-related questions
or support decision-making.
Q: Define a relational DBMS and explain how it organizes data.
A relational DBMS organizes data into two-dimensional tables (called relations), where
each table represents an entity and its attributes.
How it organizes data:
Tables (Relations): Represent entities (e.g., SUPPLIER, PART).
Rows (Records): Hold individual instances of an entity.
Columns (Fields): Represent attributes (e.g., name, ID).
Primary Key: Uniquely identifies each row in a table.
Foreign Key: Links records across different tables.
.
,Q: List and describe the three operations of a relational DBMS.
- 1. Select: Retrieves rows that meet certain criteria.
- 2. Join: Combines data from two or more tables using related fields.
- 3. Project: Extracts specific columns from a table.
Q: Explain why nonrelational databases are useful.
- - Handle large volumes of unstructured/semi-structured data (e.g., JSON, XML).
- - Scale horizontally for performance and flexibility (NoSQL).
- - Useful for real-time analytics, social media, and IoT data.
Q: Define and describe normalization and referential integrity and explain how they
contribute to a well-designed relational database.
- Normalization: The process of creating small stable data structures from complex groups
of data when designing a relational database.
- Referential Integrity: Ensures relationships between tables remain consistent using
foreign keys.
- Benefit: Supports data accuracy, avoids anomalies, and enhances database efficiency.
Q: Define and describe an entity-relationship diagram and explain its role in database
design.
- Entity-Relationship Diagram (ERD): A methodology for documenting databases illustrating
the relationship between various entities in the database
- Role: Helps design and structure databases logically before implementation, ensuring
completeness and accuracy.
Chapter 6-3: What are the principal tools and technologies for accessing information from
databases to improve business performance and decision making?
Q: Define big data and describe the technologies for managing and analyzing it.
- Big Data: Data sets with volumes so huge that they are beyond the ability of typical
relational DBMS to capture, store, and analyze. The data are often unstructured or semi
structured.
- Technologies: Include Hadoop, Spark, NoSQL databases, and cloud data platforms for
distributed storage and analysis.
, Q: List and describe the components of a contemporary business intelligence technology
infrastructure.
Contemporary business intelligence (BI) technology infrastructure refers to the
modern tools and systems used to collect, store, process, and analyze large and diverse data
from multiple sources to support better decision-making.
1. Data Warehouses & Data Marts:
Store structured current and historical data for reporting and analysis; data marts serve
specific departments.
2. Hadoop:
Processes large volumes of structured and unstructured data across distributed systems;
includes HDFS and MapReduce.
3. In-Memory Computing:
Uses RAM for fast data access and real-time analytics (e.g., SAP HANA, Oracle In-Memory).
4. Analytic Platforms:
High-speed systems optimized for large-scale data analysis (e.g., IBM PureData, NoSQL
tools).
5. Data Lakes:
Store raw, unstructured/structured data in native format for future analysis, often using
Hadoop.
Q: Describe the capabilities of online analytical processing (OLAP).
- OLAP: Capability for manipulating and analyzing large volumes of data from multiple
perspectives.
- 1. Multidimensional Analysis:
View data across multiple dimensions like product, region, and time.
2. Flexible Data Views:
“Slice and dice” to explore data from different perspectives.
3. Ad Hoc Querying:
Quickly answer spontaneous, complex business questions.
4.Comparative Analysis:
Compare actual vs. projected, or current vs. historical performance.
5. Data Cube Visualization:
