Inmon & kimball method

DATA WAREHOUSEKIMBALL OR INMONUnderstanding different approach

Understand the basics of 2 approaches

Enterprise Data Warehouse

Dimensional Modelling and Design

Understand the Similarities and Differences

In 1990 Inmon wrote a book “Building the Data Warehouse”

Inmon defines architecture for collection of disparate sources

into detailed, time variant data store( The top down approach)

In 1996 Kimball wrote “The Data Warehouse Toolkit”

Kimball updates book and defines multiple databases called

data-marts that are organized by business processes, but use

Data bus architecture (The bottom-up approach)

A Data warehouse is a collection of Enterprise wide data across line of business

and subject areas

Data is integrated using a massive database

Provides complete organizational view of the information needed to run

the business

A Data mart provides departmental view of information specific and subject

oriented

Build multiple data-marts using dimensional architecture

Provides Fact based information integrated with multiple dimensions

Data Warehouse Data Marts

Scope • Application independent

• Centralized or Enterprise

• Planned

• Specific application

• Decentralized by group

• Organic but may be planned

Data • Historical, detailed, summary

• Some de-normalization

• Some history, detailed, summary

• High de-normalization

Subjects • Multiple subjects • Single central subject area

Source • Many internal and external sources • Few internal and external sources

Pros & Cons • Flexible

• Data oriented

• Long life

• Single complex structure

• Restrictive

• Project oriented

• Short life

• Multiple simple structures that may

• form a complex structure

Bill Inmon: A data warehouse is a subject-oriented and the data in the database is

organized with data elements relating and linking together.

Time-variant: The changes to the data in the database are tracked and

recorded showing changes over time;

Non-volatile: Data in the database is never over-written or deleted -

once committed, the data is static, read-only, but retained for future

Database: The database contains data from all operational applications,

and that this data is made consistent

the data warehouse should be designed from the top-down to include all

corporate data. In this methodology, data marts are created only after the

complete data warehouse has been created.

Ralph Kimball: A proponent of the dimensional modelling and approach to

building data warehouse through data marts.

The data warehouse is nothing more than the union of all the data-marts,

Kimball indicates a bottom-up approach for data warehousing

Individual data marts are created providing views into the organizational

data in chunks

Eventually an Enterprise Data warehouse is create by combining the data

marts together using Bus architecture.

INMON KIMBALL

The warehouse is a part of Corporate information

factory consists of all Data bases.

Fact and Dimensions using Dimensional modelling

Defines database environment as

Operational: Day to day operations

Atomic: Transaction captured

Departmental: Focused

Individual: Ad-hoc

Metrics or facts and Dimension with attributes

ERD refines entities, attributes and relationships Bus architecture

Data Items sets and Data sets by department

Physical modelling to optimize performance by

de-normalizing

Does not adhere to normalization theory

Subject-Oriented, Integrated, Non-Volatile

Time-Variant, Top-Down, Enterprise Data Model

Characterizes Data marts as Aggregates

Business-Process-Oriented, Bottom-Up ,

Dimensional Model, Integration Achieved via

Conformed Dimensions, Star Model

SAP

DB2

ORACLE

Flat files

STAGING AREA

Data warehouse

DW

CUBE

USER

ACCESS

DW

SAP

DB2

ORACLE

Flat files

STAGING AREA

Data warehouse

DW

CUBEUSER

ACCESS

DW

REQUIREMENTS INMON KIMBALL

Organization requirements Strategic Tactical

Data Integration Enterprise Departmental

Structure Non metric data, meets multiple

varied information needs

Business metrics , KPI’s, Scorecards

Scalability Change of Scope and

requirements

Limited scope and volatile needs

REQUIREMENTS INMON KIMBALL

Data Stability Source systems changes frequently Stable source systems

Staff requirement Large Small

Delivery Slow and Long Quick turnaround

Cost Low upfront cost High expenditure