Chapter 4

Question 1

Online Transaction Processing (OLTP) and examples

Answer 1

-software system that processes transaction-oriented data
-completion of an activity in realtime and is not batch-processed
OLTP systems store operational data that is normalized and structured data
Examples include ticket reservation systems,
banking and point of sale systems.

Question 2

Online Analytical Processing (OLAP)

Answer 2

• used for processing data analysis queries
• They are relevant to Big Data in that they can serve as both a data source as well as a data sink that is capable of receiving data.
• OLAP systems store historical data that is aggregated and denormalized to support fast reporting

Question 3

Extract Transform Load (ETL)

Answer 3

a process of loading data from a source system

- ETL represents the main operation through which data warehouses are fed data

Question 4

Data Warehouses

Answer 4

central repository consisting of historical and current data.
Data warehouses are heavily used by BI to run various analytical queries

Question 5

Data Marts

Answer 5

subset of the data stored in a data warehouse that typically belongs to a department, division, or specific line of business

Question 6

Traditional BI

Answer 6

utilizes descriptive and diagnostic analytics
It is not “intelligent” because it only provides
answers to correctly formulated questions. Correctly formulating questions requires an
understanding of business problems and issues and of the data itself.

Question 7

Ad-hoc reporting

Answer 7

• manually processing data to produce custom made
  reports, on demand based reports
• usually on a specific area of the business, such as its marketing or supply chain management

Question 8

Dashboards

Answer 8

provide a holistic view of key business areas

Question 9

Big Data BI

Answer 9

uses data in the data warehouse and combines it with semi-structured and unstructured data source
It comprises both predictive and prescriptive analytics

Question 10

Traditional data visualization

Answer 10

provides mostly static charts and graphs in reports

Question 11

Common features of visualization tools used in Big Data

Answer 11

Aggregation, Drill-down, Filtering, Roll-up, What-if analysis

Question 12

Aggregation

Answer 12

provides a holistic and summarized view of data across multiple
contexts

Question 13

Drill-down

Answer 13

enables a detailed view of the data of interest by focusing in on a data subset from the summarized view

Question 14

Filtering

Answer 14

helps focus on a particular set of data by filtering away the data that is not of immediate interest

Question 15

Roll-up

Answer 15

groups data across multiple categories to show subtotals and totals

Question 16

What-if analysis

Answer 16

enables multiple outcomes to be visualized by enabling related
factors to be dynamically changed