The document discusses different strategies for building a data warehouse - an enterprise-wide strategy that builds a comprehensive warehouse initially versus a data mart strategy that begins with a single mart and adds more over time. It also covers key aspects of building a data warehouse like extracting, transforming, and loading data from various sources, dealing with data quality issues, and the role of metadata.

2. Introduction
To DataStage

3. Two Data Warehousing Strategies
• Enterprise-wide warehouse, top down, the Inmon
methodology
• Data mart, bottom up, the Kimball methodology
• When properly executed, both result in an enterprise-wide
data warehouse

4. The Data Mart Strategy
• The most common approach
• Begins with a single mart and architected marts are added
over time for more subject areas
• Relatively inexpensive and easy to implement
• Can be used as a proof of concept for data warehousing
• Can perpetuate the “silos of information” problem
• Can postpone difficult decisions and activities
• Requires an overall integration plan

5. The Enterprise-wide Strategy
• A comprehensive warehouse is built initially
• An initial dependent data mart is built using a subset of the data in the
warehouse
• Additional data marts are built using subsets of the data in the warehouse
• Like all complex projects, it is expensive, time consuming, and prone to
failure
• When successful, it results in an integrated, scalable warehouse

6. Data Sources and Types
• Primarily from legacy, operational systems
• Almost exclusively numerical data at the present time
• External data may be included, often purchased from third-
party sources
• Technology exists for storing unstructured data and expect
this to become more important over time

7. Extraction, Transformation, and Loading
(ETL) Processes
• The “plumbing” work of data warehousing
• Data are moved from source to target data bases
• A very costly, time consuming part of data warehousing

8. Recent Development:
More Frequent Updates
• Updates can be done in bulk and trickle modes
• Business requirements, such as trading partner access to a
Web site, requires current data
• For international firms, there is no good time to load the
warehouse

9. Recent Development: Clickstream Data
• Results from clicks at web sites
• A dialog manager handles user interactions. An ODS
(operational data store in the data staging area) helps to
custom tailor the dialog
• The clickstream data is filtered and parsed and sent to a data
warehouse where it is analyzed
• Software is available to analyze the clickstream data

10. Data Extraction
• Often performed by COBOL routines
(not recommended because of high program maintenance
and no automatically generated meta data)
• Sometimes source data is copied to the target database using
the replication capabilities of standard RDMS (not
recommended because of “dirty data” in the source systems)
• Increasing performed by specialized ETL software

11. Sample ETL Tools
• Teradata Warehouse Builder from Teradata
• DataStage from Ascential Software
• SAS System from SAS Institute
• Power Mart/Power Center from Informatica
• Sagent Solution from Sagent Software
• Hummingbird Genio Suite from Hummingbird
Communications

12. Reasons for “Dirty” Data
• Dummy Values
• Absence of Data
• Multipurpose Fields
• Cryptic Data
• Contradicting Data
• Inappropriate Use of Address Lines
• Violation of Business Rules
• Reused Primary Keys,
• Non-Unique Identifiers
• Data Integration Problems

13. Data Cleansing
• Source systems contain “dirty data” that must be cleansed
• ETL software contains rudimentary data cleansing capabilities
• Specialized data cleansing software is often used. Important
for performing name and address correction and
householding functions
• Leading data cleansing vendors include Vality (Integrity),
Harte-Hanks (Trillium), and Firstlogic (i.d.Centric)

14. Steps in Data Cleansing
• Parsing
• Correcting
• Standardizing
• Matching
• Consolidating

15. Parsing
• Parsing locates and identifies individual data elements in the
source files and then isolates these data elements in the
target files.
• Examples include parsing the first, middle, and last name;
street number and street name; and city and state.

16. Correcting
• Corrects parsed individual data components using
sophisticated data algorithms and secondary data sources.
• Example include replacing a vanity address and adding a zip
code.

17. Standardizing
• Standardizing applies conversion routines to transform data
into its preferred (and consistent) format using both standard
and custom business rules.
• Examples include adding a pre name, replacing a nickname,
and using a preferred street name.

18. Matching
• Searching and matching records within and across the parsed,
corrected and standardized data based on predefined
business rules to eliminate duplications.
• Examples include identifying similar names and addresses.

19. Consolidating
• Analyzing and identifying relationships between matched
records and consolidating/merging them into ONE
representation.

20. Data Staging
• Often used as an interim step between data extraction and
later steps
• Accumulates data from asynchronous sources using native
interfaces, flat files, FTP sessions, or other processes
• At a predefined cutoff time, data in the staging file is
transformed and loaded to the warehouse
• There is usually no end user access to the staging file
• An operational data store may be used for data staging

21. Data Transformation
• Transforms the data in accordance with the business rules
and standards that have been established
• Example include: format changes, deduplication, splitting up
fields, replacement of codes, derived values, and aggregates

22. Data Loading
• Data are physically moved to the data warehouse
• The loading takes place within a “load window”
• The trend is to near real time updates of the data warehouse
as the warehouse is increasingly used for operational
applications

23. Meta Data
• Data about data
• Needed by both information technology personnel and users
• IT personnel need to know data sources and targets;
database, table and column names; refresh schedules; data
usage measures; etc.
• Users need to know entity/attribute definitions;
reports/query tools available; report distribution information;
help desk contact information, etc.

24. Recent Development:Meta Data Integration
• A growing realization that meta data is critical to data
warehousing success
• Progress is being made on getting vendors to agree on
standards and to incorporate the sharing of meta data among
their tools
• Vendors like Microsoft, Computer Associates, and Oracle
have entered the meta data marketplace with significant
product offerings

25. Thank You !!!Thank You !!!
For More Information click below link:
Follow Us on:
http://vibranttechnologies.co.in/datastage-classes-in-mumbai.html