DataStage (IBM ETL Tool)
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- Customized corporate training for teams on DataStage (IBM ETL Tool) configuration and end-to-end implementation.
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Course Objectives
Popular Techniques Covered in This Course
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Curriculum
Module 1: IBM DataStage Architecture
- DataStage history and IBM Cloud Pak for Data positioning
- DataStage architecture: Engine, Repository, and Designer
- Job types: Server, Parallel, Sequence, and Mainframe
- DataStage Designer client: canvas and palette
- DataStage Director: monitoring and scheduling
- DataStage Administrator: project and user management
Module 2: Parallel Job Fundamentals
- Parallel job canvas: stages, links, and data flows
- Sequential File stage: reading and writing flat files
- Dataset stage: DataStage native binary format
- Transformer stage: row-level transformation logic
- Filter stage: conditional row routing
- Column import and column export stages
Module 3: Database Connectivity
- Database stages: Oracle, DB2, SQL Server, Teradata
- ODBC stage: generic database connectivity
- Lookup stage: reference data lookups
- Join stage: relational joining of datasets
- Aggregator stage: grouping and aggregation
- Sort stage: data ordering for downstream stages
Module 4: Advanced Parallel Stages
- Merge stage: sorted data merging
- Remove Duplicates stage: deduplication
- Change Capture stage: detecting changed records
- Difference stage: comparing dataset versions
- Pivot stage: row-to-column transformation
- Modify stage: schema manipulation
Module 5: Job Parameters & Sequences
- Job parameters: making jobs configurable
- Parameter sets: grouped parameter management
- Job sequences: orchestrating multiple jobs
- ExecCommand activity: running shell scripts
- Nested sequences: reusable orchestration blocks
- Scheduling with DataStage Director
Module 6: Partitioning & Performance
- Partitioning methods: hash, range, round-robin, same, entire
- Collecting: aggregating partitioned data streams
- Auto-buffer and repartitioning considerations
- Sparse lookup vs normal lookup performance
- Pushdown to database: SQL generation
- DataStage benchmark and tuning methodology
Module 7: Data Quality & Cleansing
- Data Quality stage: standardisation and matching
- Address verification stage integration
- Custom cleanse routines: BASIC transforms
- Reject handling: routing bad records
- Data Rules stage for validation
- DataStage and IBM Information Analyzer integration
Module 8: DataStage Flow Designer & Cloud
- DataStage Flow Designer: browser-based design canvas
- IBM Cloud Pak for Data: DataStage on OpenShift
- Migrating classic DataStage jobs to Flow Designer
- DataStage REST API for programmatic job management
- CI/CD for DataStage: export, import, and deployment
- DataStage performance on cloud object storage
Receive Training From Our Skilled and Effective Trainers
Our IBM DataStage instructors are DataStage-certified ETL architects with deep experience in banking, government, and telecommunications. They have designed high-throughput DataStage environments handling terabytes of daily ETL loads, with expertise in parallel job design, partitioning strategies, and DataStage migration to IBM Cloud Pak for Data.