Database Reference
In-Depth Information
object persistence
object-relational database
OLAP cube
OLAP report
OLAP server
OnLine Analytical Processing (OLAP)
online transaction processing (OLTP) system
operational system
Oracle Big Data Appliance
partitioning
Pig
PivotTable
property
R score
replication
reporting system
RDS (Relational DBMS Service)
RFM analysis
RowKey
server cluster
slowly changing dimension
SQL Server Parallel Data Warehouse
star schema
structured storage
time dimension
transactional system
virtual computer
virtual machine
virtual machine manager
Windows Azure
12.1
What are BI systems?
12.2
How do BI systems differ from transaction processing systems?
12.3
Name and describe the two main categories of BI systems.
12.4
What are the three sources of data for BI systems?
12.5
Explain the difference in processing between reporting and data mining applications.
12.6
Describe three reasons why direct reading of operational data is not feasible for BI
applications.
12.7
Summarize the problems with operational databases that limit their usefulness for BI
applications.
12.8
What are dirty data? How do dirty data arise?
12.9
Why is server time not useful for Web-based order entry BI applications?
12.10
What is click-stream data? How is it used in BI applications?
12.11
Why are data warehouses necessary?
12.12
Why do the authors describe the data in Figure 12-6 as “frightening”?
12.13
Give examples of data warehouse metadata.
12.14
Explain the difference between a data warehouse and a data mart. Use the analogy of a
supply chain.
12.15
What is the enterprise data warehouse (EDW) architecture?
12.16
Describe the differences between operational databases and dimensional databases.
12.17
What is a star schema?
12.18
What is a fact table? What type of data is stored in fact tables?
12.19
What is a measure?
12.20
What is a dimension table? What type of data is stored in dimension tables?
Search WWH ::
Custom Search