1. Chapter 4
Data Warehousing

2. Outline Definition of Data Warehouse Reasons for creating Data Marts
Benefits and characteristics of Data Warehouse
Reasons for need of data warehousing
Operational and Informational Systems
Data Warehouse vs Data Mart
Types of Systems Used
Data warehouse architectures
List four steps of data reconciliation
Design a data mart
Star Schema

3. Definition of Data Warehouse
It is a huge central database that accepts, stores and maintain data from different sources and locations.
Disparate sources may use different formats and technologies.

4. Definition of Data Mart
A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as sales, finance or marketing.
Data marts are small slices of the data warehouse.
Data marts are often built and controlled by a single department within an organization.
Given their single-subject focus, data marts usually draw data from only a few sources.
The sources could be internal operational systems, a central data warehouse, or external data.

5. Reasons for creating a data mart
Easy access to frequently needed data
Creates collective view by a group of users
Improves end-user response time
Ease of creation
Lower cost than implementing a full data warehouse
Potential users are more clearly defined than in a full data warehouse
Contains only business essential data and is less cluttered.

6. Benefits of Data Warehouse
Collect data from multiple sources into a single database so a single query engine can be used to present data.
Maintain data history, even if the source transaction systems do not.
Integrate data from multiple source systems, enabling a central view across the enterprise.
Improve data quality by flagging or even fixing bad data.
Present the organization's information consistently (constantly and reliably).
Provide a single common data model for all data of interest regardless of the data's source.
Restructure the data so that it makes sense to the business users.
Making decision–support queries are easier to write.

7. Example of using a Data Warehouse

8. Characteristics of Data Warehouse
A data warehouse is a system used for reporting and data analysis.
Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW).
Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
The data stored in the warehouse is uploaded from the operational systems.

9. Operational and Informational Systems

10. Data Warehouse Versus Data Mart

11. Types of systems used (1)
Online Analytical Processing (OLAP)
It is characterized by a low volume of transactions.
Queries are often very complex and involve aggregations.
OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas).
Online Transaction Processing (OLTP)
Characterized by a large number of transactions (INSERT, UPDATE, DELETE).
OLTP databases contain detailed and current data.
The schema used to store transactional databases is the entity model (usually 3NF).

12. Types of systems used (2)
Predictive analysis
It is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future.

13. Data Warehouse Architectures
Generic Two-Level Architecture
Independent Data Mart
Dependent Data Mart and Operational Data Store
Logical Data Mart and Real-Time Data Warehouse
Three-Layer architecture
All involve some form of
extraction, transformation and loading (ETL)

14. Generic two-level data warehousing architecture
One, company-wide warehouse
Periodic extraction  data is not completely current in warehouse

15. Data Warehousing Architecture
Independent Data Mart
Data Warehousing Architecture
Data marts: Mini-warehouses, limited in scope E T L
Separate ETL for each independent data mart
Data access complexity due to multiple data marts

16. Dependent data mart with operational data store: a three-level architecture
Single ETL for
Enterprise Data Warehouse (EDW)
Simpler data access
ODS provides option for obtaining current data
Dependent data marts loaded from EDW

17. Logical data mart and real time warehouse architecture
Near real-time ETL for
Data Warehouse
ODS and data warehouse are one and the same
Data marts are NOT separate databases,
but logical views of the data warehouse
 Easier to create new data marts

18. Three-layer data architecture for a data warehouse

19. Data Characteristics: Status vs. Event Data
Event = a database action (create/update/delete) that results from a transaction

20. Data Characteristics: Transient vs. Periodic Data
With transient data, changes to existing records are written over previous records, thus destroying the previous data content
Transient operational data

21. Data Characteristics: Transient vs. Periodic Data
Periodic data are never physically altered or deleted once they have been added to the store
Periodic warehouse
data

22. The Reconciled Data Layer
Typical operational data is:
Transient–not historical
Not normalized (perhaps due to denormalization for performance)
Restricted in scope–not comprehensive
Sometimes poor quality–inconsistencies and errors
After ETL, data should be:
Detailed–not summarized yet
Historical–periodic
Normalized–3rd normal form or higher
Comprehensive–enterprise-wide perspective
Timely–data should be current enough to assist decision-making
Quality controlled–accurate with full integrity

23. Scrub or data cleansing Transform Load and Index
The ETL Process
Capture/Extract
Scrub or data cleansing
Transform
Load and Index
ETL = Extract, transform, and load

24. Steps in data reconciliation (1)
Capture/Extract…obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse
Static extract = capturing a snapshot of the source data at a point in time
Incremental extract = capturing changes that have occurred since the last static extract

25. Steps in data reconciliation (2)
Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data quality
Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies
Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data

26. Steps in data reconciliation (3)
Transform = convert data from format of operational system to format of data warehouse
Record-level:
Selection–data partitioning
Joining–data combining
Aggregation–data summarization
Field-level:
single-field–from one field to one field
multi-field–from many fields to one, or one field to many

27. Steps in data reconciliation (4)
Load/Index= place transformed data into the warehouse and create indexes
Refresh mode: bulk rewriting of target data at periodic intervals
Update mode: only changes in source data are written to data warehouse

28. Single-field transformation
In general–some transformation function translates data from old form to new form
Algorithmic transformation uses a formula or logical expression
Table lookup–another approach, uses a separate table keyed by source record code

29. Multifield transformation
M:1–from many source fields to one target field
1:M–from one source field to many target fields

30. The star schema separates business process data into facts.
Facts hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data.
Examples of fact data include sales price, sale quantity, and time, distance, speed, and weight measurements.

31. Components of a star schema
Fact tables contain factual or quantitative data
Dimension tables contain descriptions about the subjects of the business
1:N relationship between dimension tables and fact tables
Excellent for ad-hoc queries, but bad for online transaction processing
Dimension tables are denormalized to maximize performance

32. Star schema example
Fact table provides statistics for sales broken down by product, period and store dimensions

33. Star schema with sample data