Datastage is normally used in large corporations as an interface between their different systems which also takes care of extraction, translation and the loading to the systems. The Datastage interfaces are called jobs which can be configured in such a way that they can run on single servers as well as multiple servers in a grid architecture.
- DataStage provides the ability to perform extract, transform, and load (ETL) operations from multiple sources to multiple targets, including DB2 for z/OS
- It gives developers maximum speed, flexibility and effectiveness in building, deploying, updating and managing the data integration infrastructure.
- Describe the uses of DataStage and the DataStage workflow
- Describe the Information Server architecture and how DataStage fits within it
- Describe the Information Server and DataStage deployment options
- Use the Information Server Web Console and the DataStage Administrator client to create DataStage users and to configure the DataStage environment
- Import and export DataStage objects to a file
- Import table definitions for sequential files and relational tables
- Design, compile, run, and monitor DataStage parallel jobs
- Design jobs that read and write to sequential files
- Describe the DataStage parallel processing architecture
- Design jobs that combine data using joins and lookups
- Design jobs that sort and aggregate data
- Implement complex business logic using the DataStage Transformer stage
- Debug DataStage jobs using the DataStage PX Debugger
- Read and write to database tables using DataStage ODBC and DB2 Connector stages
- Work with the Repository functions such as search and impact analysis
- Build job sequences that control batches of jobs
- Knowledge of the Windows OS.
- Familiarity with Open Database Connectivity (ODBC) and relational database access technique.
- It handles all company data and adapts to the needs;
- It offers the possibility for the organization of a complex business intelligence;
- Flexibly and scalable;
- It accelerates the running of the project;
- Easily implementable
- Introduction to DataStage
- DataStage Administration
- Working With Metadata
- Creating Parallel Jobs
- Accessing Sequential Data
- Partitioning and Collecting Algorithms
- Combining Data
- Group Processing Stages
- Transformer Stage
- Repository Functions
- Working with Relational Data
- Job Control
Project administrators and ETL developers responsible for data extraction and transformation using Data Stage.