Data Warehouse fundamentals-Part 3

OLTP

application oriented
detailed
accurate, as of the moment of access
serves the clerical community
can be updated
requirements for processing understood before initial development
compatible with the Software Development Life Cycle
performance sensitive
accessed a unit at a time
transaction driven
control of update a major concern in terms of ownership
high availability
managed in its entirety
non redundancy
static structure; variable contents
small amount of data used in a process

OLAP

subject oriented
summarized, otherwise refined
represents values over time, snapshots
serves the managerial community
is not updated
requirements for processing not completely understood before development
completely different life cycle
performance relaxed
accessed a set at a time
analysis driven
control of update no issue
relaxed availability
managed by subsets
redundancy
flexible structure
large amount of data used in a process

*OLTP*	*OLAP*
Contains Current Information	Contains previous + current information
Used to Run business	Used to analyze business
Data is updated and deleted	Data are only read
Size is less than 2TB	Size is more than 2TB

OLAP has 4 types

DOLAP (Desktop OLAP): Ex: FoxPro
ROLAP (Relational OLAP): Ex: RDBMS (Tables)-BOXIR2, COGNOS (Analysis)
MOLAP (Multi Dimensional): Ex: Cubes, BOXIR2, COGNOS (Tools)
HOLAP (Hybrid OLAP): ROLAP+MOLAP.

Data Modeling: Designing database/tables.

There are many types of data modeling but we use only 2 types.

Entity relationship (E-R) Model: cannot allow to store duplicates values
Dimensional Model: Allow Duplicate values.

Dimensional modeling has 3 phases.

Conceptual Modeling: We are analyzing number of tables and columns.
Logical modeling: Once tables are created, we are going to relate tables.
Physical modeling: We are moving all schema’s/tables to data warehouse

NOTE:

SCHEMA- Collection of fact table and dimensional table.

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