IBM Infosphere Datastage Introduction Online Training

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Welcome to IBM
Data Stage 9.1

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DATA WAREHOUSE
 A data warehouse is a copy of transaction data specifically structured for
querying and reporting.
 An expanded definition for data warehousing includes business intelligence
tools, tools to extract, transform and load data into the repository, and tools
to manage and retrieve metadata.
 This definition of the data warehouse focuses on data storage.
 A data warehouse can be normalized or de normalized.
 It can be a relational database, multidimensional database, flat file,
hierarchical database, object database, etc.
 Data warehouse data often gets changed.
 And data warehouses often focus on a specific activity or entity.

DATA WAREHOUSE

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

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 house
holding functions
 Leading data cleansing vendors include Vality (Integrity),
Harte-Hanks (Trillium), and First logic (i.e. Centric)

IBM ETL Overview

Data Stage
In its simplest form, Data Stage performs from
source systems to target systems in batch and in
real time. The data sources may include indexed
files, sequential files, relational databases, archives,
external data sources, enterprise applications and
message queues.

Data Stage
 Data Stage Administrator
 Data Stage Designer
 Data Stage Director
The Data Stage client components are:

Data Stage Administrator Designer Director
 Specify general server
defaults Add and delete
projects Set project properties
 Access Data Stage Repository
by command interface
Use Data Stage Administrator to:

 Specify how the
data is extracted
 Specify data
transformations
 Decode (de
normalize) data
going into the data
mart using
referenced lookups
 Aggregate data
 Split data into
multiple outputs on
the basis of defined
constraints
Use Data Stage
Designer to:

Use Data stage Director to run, schedule, and monitor your Data
Stage jobs. You can also gather statistics as the job runs. Also
used for looking at logs for debugging purposes.
The Data Stage Director window is divided into two panes:
 The Job Category pane lists all of the jobs in the repository.
 Right pane shows one of three views: Status view, Schedule view, or Log view.

Frequently seen Status
 1 Finished
 2 Finished (see log)
 9 Has been reset
 11 Validated OK
 12 Validated (see log)
 21 Has been reset
 99 Compiled
 0 Running
 3 Aborted
 8 Failed validation
 13 Failed validation
 96 Aborted
 97 Stopped
 98 Not Compiled

Data Stage:Getting Started
 Set up a project – Before you can create any Data Stage jobs,
you must set up your project by entering information about
your data.
 Create a job – When a Data Stage project is installed, it is empty
and you must create the jobs you need in Data Stage Designer.
 Define Table Definitions
 Develop the job – Jobs are designed and developed using the
Designer. Each data source, the data warehouse, and each
processing step is represented by a stage in the job design. The
stages are linked together to show the flow of data.

Data Stage Designer Developing a Job

Data Stage Designer Input Stage

Data Stage Designer Transformer Stage
The Transformer stage performs any data conversion required before the
data is output to another stage in the job design.
After you are done,
compile and run
the job.

Data Stage Designer

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http://kerneltraining.com/ibm-data-stage/
Questions ?

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Call us: +91 8099776681
Email: sales@kerneltraining.com
http://kerneltraining.com/ibm-data-stage/

IBM Infosphere Datastage Introduction Online Training

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