OLAP ONLINE ANALYTICAL PROCESSING

1
OLAP (ON-LINE ANALYTICAL PROCESSING)
2
ONLINE ANALYTCAL PROCESSING

• OLAP is a type of software especially developed
  for data warehouses
• Using OLAP, users can communicate with the data
  warehouse either through a GUI or Web interface,
  and quickly produce information in a variety of
  forms, including graphics
• There are two approaches to OLAP (Figure 8.20)
• ROLAP (for relational online analytical
  processing) that utilizes a standard relational
  DBMS
• MOLAP (for multidimensional online analytical
  processing) that utilizes a special
  multidimensional DBMS

3
(No Transcript)
4
CATEGORIES OF OLAP

• Relational OLAP (ROLAP)
• the tools that use variations of SQL and view the
  database as a traditional relational database
• ROLAP tools access the data warehouse or data
  mart directly
• Multidimensional OLAP (MOLAP)
• load data into an intermediate structure (3 or
  higher dimensional array).
• Cube structure
• A MOLAP data mart is created by extracting data
  from the data warehouse or data mart and then
  storing the data in a specialized separate data
  store.
• Data can be viewed only through a
  multidimensional structure.

5
ROLAP and MOLAP

• Both OLAP types include a data warehouse server
  and a second server that houses OLAP software
• A major difference is that the MOLAP workstation
  includes a downloaded multidimensional database
• The data in this database has already been
  formatted in various dimensions so that it may be
  made available quickly rather than go through
  time-consuming analyses
• Figure 7.1 illustrates a report that is the type
  that ROLAP can easily prepare
• MOLAP can produce information in many dimensions
• Figure 7.2 illustrates a summary report in four
  dimensions store type, product, age, and gender

6
Figure 7.1
7
Figure 7.2
8
On-Line Analytical Processing (OLAP)--cont

• OLAP is to contrast OLTP (Online Transaction
  Processing)
• Synonym for OLAP - multidimensional analysis
• An example of a data cube (or multidimensional
  view) - figure 7-0
• This view corresponds to star schema - figure 7-1

9
Figure 7-0 Slicing a data cube
10
Figure 7-1 Star schema example
Fact table provides statistics for sales broken
down by product, period and store dimensions
11
OLAP OPERATIONS

• Cube slicing come up with 2-D view of data
  (Figure 7-2)
• Drill-down going from summary to more detailed
  views (7-3)

12
Figure 7-2 Slicing a data cube
Slicing the data cube to produce a simple
two-dimensional table or view. This slide is
for the product named shoes. The resulting
table shows the three measures (units, revenues,
and cost) for this product by period (month)
13
Summary report
Figure 7-3 Example of drill-down
Using OLAP tool, this breakdown can be easily
obtained using a point click
Drill-down with color added
14

• Higher Level
• of Aggregation

• Sales Channel
• Region
• Country
• State/Province
• Location Address
• Sales Representative

Roll-up
Drill-down
Lower Level of Detail
15
On-Line Analytical Processing..cont

• Multidimensional Data Analysis Techniques
• The processing of data in which data are viewed
  as part of a multidimensional structure.
• Multidimensional view allows end users to
  consolidate or aggregate data at different
  levels.
• Multidimensional view allows a business analyst
  to easily switch business perspectives.
• Figure 7-4

16
Figure 7-4 Operational Vs. Multidimensional
View Of Sales
17
On-Line Analytical Processing

• Additional Functions of Multidimensional Data
  Analysis Techniques
• Advanced data presentation functions
• Advanced data aggregation, consolidation, and
  classification functions
• Advanced computational functions
• Advanced data modeling functions

18
Figure 7.5 Integration Of OLAP With A
Spreadsheet Program
19
On-Line Analytical Processing

• OLAP Architecture
• Three Main Modules
• OLAP Graphical User Interface (GUI)
• OLAP Analytical Processing Logic
• OLAP Data Processing Logic
• OLAP systems are designed to use both operational
  and Data Warehouse data.
• Figure 7-6

20
Figure 7.6 OLAP Server Arrangement
21
Figure 7.7 OLAP Server With Multidimensional
Data Store Arrangement
22
Figure 7.8 OLAP Server With Local Mini Data-Marts
23
Figure 7.9 A Typical ROLAP Client/Server
Architecture