This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.
|08/11/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led|
|08/29/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led|
|09/29/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led|
|11/03/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led|
|12/01/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led|
- Use Connector stages to read from and write to database tables
- Handle SQL errors in Connector stages
- Use Connector stages with multiple input links
- Use the File Connector stage to access Hadoop HDFS data
- Optimize jobs that write to database tables
- Use the Unstructured Data stage to extract data from Excel spreadsheets
- Use the Data Masking stage to mask sensitive data processed within a DataStage job
- Use the Hierarchical stage to parse, compose, and transform XML data
- Use the Schema Library Manager to import and manage XML schemas
- Use the Data Rules stage to validate fields of data within a DataStage job
- Create custom data rules for validating data
- Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions
DataStage Essentials course or equivalent.
Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources.