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Lesson 1:  InfoSphere DataStage
o    Understanding the IBM InfoSphere DataStage
o    the Job life cycle to develop, test, deploy and run data jobs
o    high performance parallel framework
o    real-time data integration.    

Lesson 2: DataStage Features
o    Introduction to the design elements
o    various DataStage jobs
o    creating massively parallel framework
o    scalable ETL features
o    working with DataStage jobs.

Lesson 3: DataStage Job
o    Understanding the DataStage Job
o    creating a Job that can effectively extract
o    transform and load data
o    cleansing and formatting data to improve its quality.
    
Lesson 4: Parallelism, Partitioning and Collecting
o    Learning about data parallelism – pipeline parallelism and partitioning parallelism
o    the two types of data partitioning – Key-based partitioning and Keyless partitioning
o    detailed understanding of partitioning techniques like round robin, entire, hash key, range, DB2 partitioning
o    data collecting techniques and types like round robin, order, sorted merge and same collecting methods.

Lesson 5:  Job Stages of InfoSphere DataStage
o    Understanding the various job stages – data source, transformer, final database
o    the various parallel stages – general objects, debug and development stages
o    processing stage
o    file stage types
o    database stage
o    real time stage
o    restructure stage
o    data quality and sequence stages of InfoSphere DataStage.
    
Lesson 6: Stage Editor And Sequential File
o    Understanding the parallel job stage editors
o    the important types of stage editors in DataStage.
o    Working with the Sequential file stages
o    understanding runtime column propagation
o    working with RCP in sequential file stages
o    using the sequential file stage as a source stage and target stage.

Lesson 7: Dataset and Fileset And Sample Job Creation
o    Understanding the difference between dataset and fileset and how DataStage works in each scenario.
o    Creating of a sample DataStage job using the dataset and fileset types of data.
    
Lesson 8: Properties of Sequential File stage and Data Set Stage, Lookup File Set Stage
o    Learning about the various properties of Sequential File Stage and Dataset stage.
o    Creating a lookup file set
o    working in parallel or sequential stage
o    learning about single input and output link.

Lesson 9: Transformer Stage
o    Studying the Transformer Stage in DataStage
o    the basic working of this stage
o    characteristics -single input
o    any number of outputs and reject link
o    how it differs from other processing stages
o    the significance of Transformer Editor, and evaluation sequence in this stage.
    
Lesson 10: Transformer Stage Functions & Features
o    Deep dive into Transformer functions – String
o    type conversion
o    null handling
o    mathematical
o    utility functions
o    understanding the various features like constraint
o    system variables
o    conditional job aborting
o    Operators and Trigger Tab.

Lesson 11: Transformer Stage Functions & Features
o    Deep dive into Transformer functions – String
o    type conversion
o    null handling
o    mathematical
o    utility functions
o    understanding the various features like constraint, system variables
o    conditional job aborting
o    Operators and Trigger Tab.    

Lesson 12: Teradata Enterprise Stage And Different Stages Of Processing
o    Connecting to the Teradata Enterprise Stage
o    properties of connection.
o    Deep learning of the various stages in DataStage, the importance of Copy
o    Filter and Modify stages to reduce number of Transformer Stages.

Lesson 13: Parameters and Value File
o    Understanding Parameter Set
o    storing DataStage and Quality Stage job parameters and default values in files
o    the procedure to deploy Parameter Sets function and its advantages.