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IBM Infosphere Datastage Training
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About Course
💻 Course Overview
The IBM InfoSphere DataStage Online Training is designed to help learners master ETL (Extract, Transform, Load) processes using IBM’s powerful DataStage tool, a key component of the IBM InfoSphere Information Server suite. This course provides a deep understanding of data integration, transformation, and data warehousing techniques used in enterprise data management.
🎯 Key Learning Outcomes
✅ Understand IBM InfoSphere DataStage architecture and components
✅ Learn to create, design, and run ETL jobs for data extraction, transformation, and loading
✅ Work with different stages, such as Sequential, Transformer, Aggregator, and Lookup
✅ Implement parallel processing for high-performance data integration
✅ Manage metadata, repositories, and reusable job components
✅ Apply job sequencing, error handling, and debugging techniques
✅ Integrate DataStage with databases, XML, and cloud data sources
👩💻 Who Should Attend
🔹 ETL Developers
🔹 Data Engineers
🔹 Database Administrators
🔹 Business Intelligence Professionals
🔹 Data Integration Specialists
🧠 Skills You Will Gain
✨ Mastery in IBM InfoSphere DataStage ETL development
✨ Ability to design and optimize data pipelines
✨ Experience with data transformation and cleansing
✨ Knowledge of parallel job design and performance tuning
✨ Understanding of data warehouse architecture
✨ Preparation for IBM Certified DataStage Developer certification
🏆 Course Benefits
🌟 Learn from industry experts with real-time project experience
🌟 Hands-on labs for building and deploying ETL jobs
🌟 Gain in-depth knowledge of IBM InfoSphere architecture
🌟 Improve your career prospects in data engineering and BI
🌟 Get ready for IBM Certified DataStage Developer exam
IBM Infosphere Datastage Training Course Syllabus
Module-1 Data Warehouse Fundamentals
✔ An Introduction of Data warehousing
✔ Purpose of Data warehouse
✔ Data ware Architecture
✔ OLTP Vs Data warehouse Applications
✔ Data Marts
✔ Data warehouse Lifecycle
✔ SDLC
Module-2 Data Modeling
✔ Introduction of Data Modeling
✔ Entity-Relationship Model
✔ Dimensions and Fast Tables
✔ Logical Modeling
✔ Physical Modeling
✔ Schemas Like Star Schema & Snowflake Schemas
✔ Fact less Fact Tables
Module-3 Process of ETL (Extraction, Transaction @Load)
✔ Introduction of Extraction, Transformation, and Loading
✔ Types of ETL tools
✔ Key tools in the market
Module-4 IBM Infosphere Datastage Installation process
✔ Windows server
✔ Oracle
✔ .NET
✔ Datastage 7.5X2 & 8x&9x
Module-5 Difference
✔ Server jobs & Parallel jobs
Module-6 Components in Datastage
✔ Administrator client
✔ Designer client
✔ Director client
✔ Import/export manager
✔ Multi-client manager
✔ Console for IBM information server
✔ Web console for IBM information server
Module-7 Introduction to IBM infoSphere Datastage and Quality Stage
✔ Datastage Introduction
✔ IBM Information server Architecture
✔ IBM Data Quality Architecture
✔ Enterprise Information Integration
✔ Web Sphere DataStage Components
Module-8 IBM Infosphere Datastage Designer
✔ About Web Sphere DataStage Designer
✔ Partitioning Methods
✔ Partitioning Techniques
✔ Designer Canvas
✔ Central Storage
✔ Job Designing
✔ Creating the Jobs
✔ Compiling and Run the Jobs
✔ Exporting and importing the jobs
✔ Parameter passing
✔ System(SMP) & Cluster system(MPP)
✔ Importing Method(Flat file, Txt, Xls, and Database files)
✔ OSH Importing Method
✔ Configuration file
Module-9 Parallel Palette
✔ Databases stages
✔ Oracle Database
✔ Dynamic RDBMS
✔ ODBC
✔ SQL Server
✔ Teradata
✔ File Stages
✔ Sequential File
✔ Dataset
✔ Lookup File set
✔ Dev/Debug Stages
✔ Peek
✔ Head
✔ Tail
✔ Row Generator
✔ Column Generator
✔ Processing Stages
✔ Slowly changing dimension stage
✔ Slowly changing dimensions implementation
✔ Aggregator
✔ Copy
✔ Compress
✔ Expand
✔ Filter
✔ Modify
✔ Sort
✔ Switch
✔ Lookup
✔ Join
✔ Marge
✔ Change Capture
✔ Change Apply
✔ Compare
✔ Difference
✔ Funnel
✔ Remove Duplicate
✔ Surrogate Key Generator
✔ Pivot stage
✔ Transformer
✔ Containers
✔ Shared Containers
✔ Local Containers
Module-10 IBM Infosphere Datastage – Director
✔ About DS Director
✔ Validation
✔ Scheduling
✔ Status
✔ View logs
✔ Monitoring
✔ Suppress and Demote the Warnings
✔ Peek view
Module-11 IBM Infosphere Datastage Administrator
✔ Create Project
✔ Delete Project
✔ Protect Project
✔ Environmental variables
✔ Auto purge
✔ RCP
✔ OSH
✔ Commands Execute
✔ Multiple Instances
✔ Job Sequence Settings
Module-12 Job Sequence Area
✔ Job Activity
✔ Job sequencer
✔ Start loop Activity
✔ End loop Activity
✔ Notification Activity
✔ Terminator Activity
✔ Nested Condition Activity
✔ Exception handling Activity
✔ Execute Command Activity
✔ Wait for the file Activity
✔ User variable Activity
✔ Adding Check Points
- Create Project
- Delete Project
- Protect Project
- Environmental variables
- Auto purge
- RCP
- OSH
- Commands Execute
- Multiple Instances
- Job Sequence Setting
Module-13 Basic IBM Infosphere Datastage Quality stage
✔ Data Quality
✔ Data Quality Stages
✔ Investigate Stage
✔ Standardize Stage
✔ Match Frequency Stage
✔ Reference Match Stage
✔ Unduplicated Match Stage
✔ Survive Stage
IBM Infosphere Datastage Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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- Complete set of live-online training session recorded videos.
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- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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IBM Infosphere Datastage Online Training FAQ'S
What is IBM InfoSphere DataStage, and how is it used in data integration?
Explain that DataStage is an ETL (Extract, Transform, Load) tool within IBM InfoSphere used to design, develop, and run data integration jobs that move and transform data between sources and targets.
What are the different types of DataStage jobs?
Mention:
Server Jobs – Used for sequential processing.
Parallel Jobs – Designed for large-scale data using parallelism.
Sequencer Jobs – Control execution flow of other jobs.
What is parallel processing in DataStage, and why is it important?
Explain that parallel processing allows DataStage to split data across multiple processors, improving performance and scalability for large datasets.
How do you handle errors and exceptions in DataStage jobs?
Discuss techniques such as:
1.Using Reject Links for error data capture.
2.Applying Exception Handling stages like Transformer and Sequencer.
3.Implementing job logs and triggers for debugging.
What are some performance tuning methods in DataStage?
Include methods like:
1.Using proper partitioning and buffering.
2.Minimizing use of lookups and unnecessary conversions.
3.Optimizing Transformer logic and using runtime parameters.
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