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Aboutsss Course:

This course covers the basic of Data Warehousing fundamentals, ETL Fundamentals and focuses on implementing Extract, Transform and Load strategy using IBM DataStage. Learn the real work Data Transformation rules, Data Quality Check, Basic and Advance Business Rules, Data Loading Strategies and optimized ETL solutions. The training is full of real time case studies, Problem/Solutions based model and based on current job requirements.

Duration : 30 hours

Fee2: 319

Job Trends

Who Should Learn?

This course will help project administrator and ETL developers to acquire the parallel jobs in data stage.


  1. Basic knowledge of Windows operating system
  2. Familiarity with database access techniques

Delivery Methodology

We are using an experiential delivering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course.

Class Delivery

Live Interactive classes with expert

Training Calender

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No Schedule Available
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+91 769-409-5404

  • 24 hours on-demand video
  • Articles
  • Coding Exercises
  • Full lifetime access
  • Certificate of Completion


ETL Solution using IBM DataStage

IBM Info sphere Data Stage and Quality Stage 11.3



Data Warehouse Fundamentals

An introduction to Data Warehousing purpose of Data Warehouse Data Warehouse Architecture Operational Data Store OLTP Vs Warehouse Applications Data Marts- Data marts Vs Data Warehouses Data Warehouse Life cycle .

Data Modeling

Introduction to Data Modeling Entity Relationship model (E-R model) Data Modeling for Data Warehouse, Normalization process Dimensions and fact tables Star Schema and Snowflake Schemas.

ETL Design Process

Introduction to Extraction, Transformation & Loading- Types of ETL Tools Key tools in the market.

Introduction to Data stage Version 11.3

Datastage introduction IBM information Server architecture DataStage components DataStage main functions Client components.

Data Stage Designer

Introduction to Data stage Designer Importance of Parallelism Pipeline Parallelism Partition Parallelism Partitioning and collecting Symmetric Multi Processing (SMP) Massively Parallel Processing (MPP) Partition techniques Data stage Repository Palette Passive and Active stages Job design overview Designer work area Annotations Creating jobs Importing flat file definitions Managing the Metadata environment Dataset management Deletion of Dataset Routines Arguments.

Working with Parallel Job Stages

Database Stages

Oracle connector Teradata Connector ODBC

File Stages

Sequential file Dataset File set Lookup file set-Complex Flat File Stage

Processing Stages

Copy Filter Funnel Sort- Remove duplicate Aggregator Modify Compress Expand Decode Encode Switch Pivot stage -Lookup Join Merge look up, join and merge change capture Change apply Compare Difference Surrogate key generator Transformer

Debug Stages

Head Tail Peek Column generator Row generator Write Range Map Stage.

Routines creation

Advanced Stages in Parallel Jobs (Version 11.3)

Range Look process Surrogate key generator stage Slowly changing dimension stage iway stage FTP stage-Pivot Enterprise Job performance analysis Resource estimation- Performance Optimizer Slowly Changing Dimensions implementation , Transformer stage looping condition, Transformer stage Last Row handling

Datastage Director

Introduction to Data stage Director Validating Data stage Jobs Executing Data stage jobs Job execution status Monitoring a job Job log view job scheduling Creating Batches Scheduling batches.

DATASTAGE Administrator

Data stage project Administration - Editing projects and Adding Projects Deleting projects Cleansing up project files Environmental VariablesEnvironment management Auto purging Rutime Column Propagation(RCP) Add checkpoints for sequencer NLS configuration Generated OSH (Orchestra Engine) System formats like data, timestamp Project protect Version details.


Job Sequencers

Arrange job activities in Sequencer Triggers in Sequencer Restablity Recoverability Notification activity Terminator activity Wait for file activity Start Look activity Execute Command activity Nested Condition activity Exception handling activity User Variable activity End Loop activity Adding Checkpoints

Info sphere Quality Stage


  • Why Data Quality

  • Data Quality Challenges

  • Types of Data Quality Tools Provided by IBM

  • Differences between IA and QS

  • Quality stage Architecture

  • Data stage Quality Stages

    • Investigate Stage

      • Default Class Descriptions

      • Word Investigation

      • Character Discrete Investigation

      • Character concatenate investigation


    • Standardize Stage

      • Standardize Process

      • Domain Specific Rule sets

      • Domain Preprocessing Rule sets

      • Creation of Custom Rule sets with Examples( SEPLIST/STRIPLIST,Classification file,Dictionary Files,Pattern Action File,Lookup Tables,Override Tables )

      • Introduction to Pattern Action language

      • Types Of Patterns ( Conditional and Unconditional )

      • Build customized Action statements using PAL with Examples

      • Standardize Quality Assessment Report ( SQA )


    • Match Stage

      • Match Process

      • Creation of Match Passes

      • Match Frequency Stage & Reports

      • Unduplicate Match Stage with Examples

      • Reference Match Stage with Examples


    • Survive Stage

      • Importance of Survive Stage

      • Build Survive Process

      • Implementation of Survive Rules


  • Explanation about the entire Data Quality Life Cycle


IBM Information Server Administration

IBM Info sphere Data Stage administration Opening the IBM Information Server Web console setting up a project ion the console Customizing the project dashboard Setting up security Creating users in the console Assigning security roles to users and groups Managing licenses Managing active sessions Managing logs Managing schedules Backing up and restoring IBM Information Server.






  • Project architecture and BRD discussion

  • Dimensional tables and fact tables with modeling

  • Flow of subject area discussion

  • Design of HLDÔŅĹs and LLDÔŅĹs for a project

  • Project flow-job design process with ETL Documents

  • Complex jobs discussion-unit testing process

  • System & User Acceptance & Regression & End-to-End Testing

  • Deployment Process of code to different phases

  • Creation of job design documents or overview docs, tech specs

  • Production support process

  • UNIX scripting for automation of code

  • Discussion of scheduling process with Control-M/Autosys

  • Fixing of Defects and Problem Tickets and Incidents


Additional Features


  • Data stage project on Banking & Insurance & Health Care domain.

  • Data stage Certification Guidance.

  • Performance Tunning of Parallel Jobs.

  • Datastage Installation process and setup.

  • Well Versed Materials Which Covers Data warehousing Basics, Datastage Concepts UnixCommands, Shall Script, Databases.

Use Case

Use Case 1


Benefits of Certificate

Certification demonstrates your dedication, motivation and technical knowledge on a specific platform. Having a certification shows that you not only possess comprehensive knowledge of that technology but you also care enough about your own career to spend the time and money to get the certification.

We are welcoming our Students or professionals to participate in our professional online courses. We are offering great variety of online training programs and professional courses that you can always find as desired. After the completion of training program they will receive a certificate from BISP. As a Certified professional you can apply that knowledge in your future profession and enjoy with better salaries & career prospects.

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