Datastage Training

Datastage Class Chennai provides real-time and placement focused Datastage training in chennai with real-time project scenarios. We can guarantee classes that makes you as a ETL Expert. The Best Datastage Training course that is exclusively designed with Basics through Advanced Data warehousing Concepts. Datastage Certification and Interview Guidance are provided during the course. All our training sessions are Completely Practical and Real Time.

Special Offer for this month :: Analytic SQL + Datastage + UNIX with a discount price of Rs. 15,000/-

Learn how to use Datastage from beginner level to advanced techniques which is taught by experienced working professionals. With our Datastage Training in Chennai you will learn concepts in expert level with practical scenarios.

Datastage Training in Chennai

Best Institute for Datastage Training in Chennai provided by Real time working Experts. Datastage Training, ETL Tool Training, Data Modeling Training, Analytic SQL Training with real-world ETL process implementations organized in Datastage training classes.
Greens Technology in Adyar is the Best Datastage training institute in Chennai offers Hands-On Datastage courses in Chennai with Job Placement by ETL professionals having Datastage projects experience using ETL and Business Intelligence for more than 10 years. All our sessions are completely practical and interactive paired with Realtime Methodologies, Project Scenarios and Case studies exclusively on Datastage.

Datastage Training Course Content

Introduction to Data warehousing

  • What is Data warehousing
  • Architecture of Data warehousing
  • Definitions
  • ETL Process
  • Types of Tables in D/W
  • Types of FACTS tables
  • Types of DIMENSION tables
  • Types of Schemas in D/W
  • What is Data Mart
  • Warehouse Approaches

Data Modelling

  • Introduction to Data Modelling
  • Entity Relationship model (E-R model)
  • Data Modeling for Data Warehouse
  • Dimensions and fact tables
  • Star Schema and Snowflake Schemas
  • Coverage Tables
  • Fact less Tables
  • What to look for in modelling tools
  • Modelling tools

ETL Design process

  • Introduction to Extraction, Transformation & Loading
  • Types of ETL Tools
  • What to look for in ETL Tools
  • Key tools in the market
  • ETL Trends & New Solution Options

Data stage installation

  • Datastage Installation
  • Prerequisites to install Datastage
  • Installation process


Introduction to Datastage version 8.x

  • Datastage Introduction
  • IBM Information Server architecture
  • DataStage within the IBM Information Server architecture
  • Datastage components
  • DataStage main functions
  • Client components

Datastage Administrator:

  • Datastage project Administration
  • Editing projects and Adding projects
  • Deleting projects
  • Cleansing up project files
  • Auto purging
  • Permissions to users
  • Runtime Column Propagation
  • Enable Remote Execution of Parallel jobs
  • Add checkpoints for sequencer
  • Project protect
  • .APT Config file

Datastage Designer:

  • Introduction to Datastage Designer
  • Importing table definitions
  • Importing flat file definitions
  • Managing the meta data environment
  • Dataset management
  • Deletion of Dataset
  • Importing jobs
  • Exporting jobs(Back up)
  • Configuration file view
  • Explanation of Menu Bar
  • Palette
  • Passive stages
  • Active stages
  • Database stages
  • Debug stages
  • File stages
  • Processing stages
  • Mutiple Instances
  • Runtime Column Propagation(RCP)
  • Job design overview
  • Designer work area
  • Annotations
  • Creating jobs,deleting jobs
  • Compiling jobs
  • Batch compiling
  • Aggregator stage ,Copy stage
  • Change Capture stage,Compress stage
  • Filter stage,Funnel stage
  • Modify stage
  • Join stage,Lookup stages
  • Difference between join and Lookup stages
  • Merge stage
  • Difference between Lookup and Merge stages
  • Remove duplicate stage
  • Sort stage,Pivot stage
  • Surrogate key stage, switch stage
  • Types of Lookups
  • Types of Transformer stages
  • Basic transformer stage
  • Transformer stage
  • Null handling in Transformer stage
  • If Then Else in Transformer
  • Stage variables
  • Constraints
  • Derivations
  • Peek stage, Head stage, Tail stage
  • Job properties
  • Local variables
  • Functions in Transformers
  • String,Date,Null handling functions
  • All properties in all stages
  • Slowly changing Dimensions (SCD)
  • SCD Type-1
  • SCD Type-2
  • SCD Type-3
  • Implementation of SCD T ype-1 in Datastage
  • Implementation of SCD T ype-2 in Datastage

Datastage Director:

  • Introduction to Datastage director
  • Datastage Director window
  • Jobs status view
  • Datastage director options
  • Running Datastage jobs
  • Validating a job
  • Running a job
  • Batch Running
  • Stopping a job and resetting job
  • Monitoring a job
  • Job scheduling
  • Unscheduling a job
  • Rescheduling a job
  • Deleting a job
  • Unlocking jobs
  • View Logfile
  • Clear log
  • Fatal error description
  • Warning description
  • Info description
  • Difference between Compile and Validate
  • Difference between Validate and Run


  • Arrange job activities in Sequencer
  • Triggers in Sequencer
  • Reset method
  • Recoverability
  • Notification Activity
  • Terminator Activity
  • Wait for file Activity
  • Start Look Activity
  • Execute Command Activity
  • Sequencer

  • Reusability
  • Minimizing complexity
  • Local container
  • Shared container
  • Some jobs in container


  • Parallel
  • Pipeline Parallelism
  • Partition Parallelism
  • Partitioning and Collecting
  • Configuration file
  • Fastname, Pools, Resource Disk, Resource Scratch Disk
  • Running Job with different nodes
  • Symmetric Multi Processing
  • Massively Parallel Processing
  • Partition techniques
  • Round Robin
  • Random
  • Hash
  • Entire
  • Same
  • Modulus
  • Range
  • DB2
  • Auto
  • Datastage components
  • Server components
  • Clients components
  • Datastage Server
  • Datastage Repository
  • Naming Standards of jobs
  • Document preparation
  • ETL specs preparation
  • Unit testcases preparation


    Potential Migration approach and techniques

    • Datastage version upgrade migration (ie DS 7.5.2 to 8.1\8.5\ 8.7\9.1)
    • Datastage Server job to Parallel Job migration
    • ETL tool migration(ie Informatics\Abinito to Datastage)
    • DWH Database Migration (ie Oracle to Teradata )
    • DWH concept migration (SCD –1 Type structure to Type 2)


    • Estimation Templates (Simple /Medium/Complex Job)
    • Test case Vs Bug report templates
    • Check list for Datastage developers

    Other Advantages:

    • Real-time Scenarios
    • Deployment Process
    • Project Experience
    • Resume Preparation
    • Mock Interviews
    • FAQ’s.

    Analytic SQL for Data Warehousing

    • Course Objectives, Course Agenda and Class Account Information
    • Describe the Schemas and Appendices used in the Lesson
    • Overview of SQL*Plus Environment
    • Overview of SQL Developer
    • Overview of Analytic SQL
    • Oracle Database SQL and Data Warehousing Documentation

    Grouping and Aggregating Data Using SQL

    • Generating Reports by Grouping Related Data
    • Review of Group Functions
    • Reviewing GROUP BY and HAVING Clause
    • Using the ROLLUP and CUBE Operators
    • Using the GROUPING Function
    • Working with GROUPING SET Operators and Composite Columns
    • Using Concatenated Groupings with Example

    Hierarchical Retrieval

    • Using Hierarchical Queries
    • Sample Data from the EMPLOYEES Table
    • Natural Tree Structure
    • Hierarchical Queries: Syntax
    • Walking the Tree: Specifying the Starting Point
    • Walking the Tree: Specifying the Direction of the Query
    • Using the WITH Clause
    • Hierarchical Query Example: Using the CONNECT BY Clause

    Working with Regular Expressions

    • Introducing Regular Expressions
    • Using the Regular Expressions Functions and Conditions in SQL and PL/SQL
    • Introducing Metacharacters
    • Using Metacharacters with Regular Expressions
    • Regular Expressions Functions and Conditions: Syntax
    • Performing a Basic Search Using the REGEXP_LIKE Condition
    • Finding Patterns Using the REGEXP_INSTR Function
    • Extracting Substrings Using the REGEXP_SUBSTR Function

    Analyzing and Reporting Data Using SQL

    • Overview of SQL for Analysis and Reporting Functions
    • Using Analytic Functions
    • Using the Ranking Functions
    • Using Reporting Functions

    Performing Pivoting and Unpivoting Operations

    • Performing Pivoting Operations
    • Using the PIVOT and UNPIVOT Clauses
    • Pivoting on the QUARTER Column: Conceptual Example
    • Performing Unpivoting Operations
    • Using the UNPIVOT Clause Columns in an UNPIVOT Operation
    • Creating a New Pivot Table: Example

    Pattern Matching using SQL

    • Row Pattern Navigation Operations
    • Handling Empty Matches or Unmatched Rows
    • Excluding Portions of the Pattern from the Output
    • Expressing All Permutations
    • Rules and Restrictions in Pattern Matching
    • Examples of Pattern Matching

    Modeling Data Using SQL

    • Using the MODEL clause
    • Demonstrating Cell and Range References
    • Using the CV Function
    • Using FOR Construct with IN List Operator, incremental values and Subqueries
    • Using Analytic Functions in the SQL MODEL Clause
    • Distinguishing Missing Cells from NULLs
    • Using the UPDATE, UPSERT and UPSERT ALL Options

    Data Warehousing Fundamentals

      Data Warehousing, Business Intelligence, OLAP, and Data Mining

      • Data Warehouse Definition and Properties
      • Data Warehouses, Business Intelligence, Data Marts, and OLTP
      • Typical Data Warehouse Components
      • Warehouse Development Approaches
      • Extraction, Transformation, and Loading (ETL)
      • The Dimensional Model and Oracle OLAP
      • Oracle Data Mining

      Defining Data Warehouse Concepts and Terminology

      • Data Warehouse Definition and Properties
      • Data Warehouse Versus OLTP
      • Data Warehouses Versus Data Marts
      • Typical Data Warehouse Components
      • Warehouse Development Approaches
      • Data Warehousing Process Components
      • Strategy Phase Deliverables
      • Introducing the Case Study: Roy Independent School District (RISD)

      Business, Logical, Dimensional, and Physical Modeling

      • Data Warehouse Modeling Issues
      • Defining the Business Model
      • Defining the Logical Model
      • Defining the Dimensional Model
      • Defining the Physical Model: Star, Snowflake, and Third Normal Form
      • Fact and Dimension Tables Characteristics
      • Translating Business Dimensions into Dimension Tables
      • Translating Dimensional Model to Physical Model

      Database Sizing, Storage, Performance, and Security Considerations

      • Database Sizing and Estimating and Validating the Database Size
      • Oracle Database Architectural Advantages
      • Data Partitioning
      • Indexing
      • Optimizing Star Queries: Tuning Star Queries
      • Parallelism
      • Security in Data Warehouses
      • Oracle’s Strategy for Data Warehouse Security

      The ETL Process: Extracting Data

      • Extraction, Transformation, and Loading (ETL) Process
      • ETL: Tasks, Importance, and Cost
      • Extracting Data and Examining Data Sources
      • Mapping Data
      • Logical and Physical Extraction Methods
      • Extraction Techniques and Maintaining Extraction Metadata
      • Possible ETL Failures and Maintaining ETL Quality
      • Oracle’s ETL Tools: Oracle Warehouse Builder, SQL*Loader, and Data Pump

      The ETL Process: Transforming Data

      • Transformation
      • Remote and Onsite Staging Models
      • Data Anomalies
      • Transformation Routines
      • Transforming Data: Problems and Solutions
      • Quality Data: Importance and Benefits
      • Transformation Techniques and Tools
      • Maintaining Transformation Metadata

      The ETL Process: Loading Data

      • Loading Data into the Warehouse
      • Transportation Using Flat Files, Distributed Systems, and Transportable Tablespaces
      • Data Refresh Models: Extract Processing Environment
      • Building the Loading Process
      • Data Granularity
      • Loading Techniques Provided by Oracle
      • Postprocessing of Loaded Data
      • Indexing and Sorting Data and Verifying Data Integrity

      Refreshing the Warehouse Data

      • Developing a Refresh Strategy for Capturing Changed Data
      • User Requirements and Assistance
      • Load Window Requirements
      • Planning and Scheduling the Load Window
      • Capturing Changed Data for Refresh
      • Time- and Date-Stamping, Database triggers, and Database Logs
      • Applying the Changes to Data
      • Final Tasks

      Materialized Views

      • Using Summaries to Improve Performance
      • Using Materialized Views for Summary Management
      • Types of Materialized Views
      • Build Modes and Refresh Modes
      • Query Rewrite: Overview
      • Cost-Based Query Rewrite Process
      • Working With Dimensions and Hierarchies

      Leaving a Metadata Trail

      • Defining Warehouse Metadata
      • Metadata Users and Types
      • Examining Metadata: ETL Metadata
      • Extraction, Transformation, and Loading Metadata
      • Defining Metadata Goals and Intended Usage
      • Identifying Target Metadata Users and Choosing Metadata Tools and Techniques
      • Integrating Multiple Sets of Metadata
      • Managing Changes to Metadata

      Data Warehouse Implementation Considerations

      • Project Management
      • Requirements Specification or Definition
      • Logical, Dimensional, and Physical Data Models
      • Data Warehouse Architecture
      • ETL, Reporting, and Security Considerations
      • Metadata Management
      • Testing the Implementation and Post Implementation Change Management

      UNIX Shell Scripting

      Unix Command Review

      • Basic Unix commands
      • General commands
      • File and directory handling commands
      • Filename generation characters
      • I/O Redirection features
      • Other commands

      Getting Started

      • What is a shell script?
      • Development guidelines
      • Creating and editing shell scripts
      • Naming and storing shell scripts
      • Executing shell scripts
      • Exercise: Write a simple shell script

      Using Variables

      • Environment variables
      • Local variables
      • Assigning values to variables
      • Assessing variable values
      • Using quotes
      • Delimiting variable names
      • Echo control sequences
      • Exercise: Add variables to a script

      Integer Arithmetic

      • Using the expr command
      • Using the (( )) notation
      • Exercise: Add integer arithmetic to a shell script

      Handling Run Time Data

      • The read command
      • Command line arguments
      • Exercise: Writing a generic shell script
      • Exercise: Writing an interactive shell script

      Condition Execution

      • The if statement
      • The test command
      • Other test notations
      • Default and substitute variables
      • Exit status codes
      • Exercise: Adding validation to previous scripts

      Loop Constructs

      • The while loop
      • The until loop
      • The for loop
      • The while true and until false loops
      • Loop control commands
      • Exercise: Enhancing the previously written scripts
      • Exercise: Writing a guess-the-number game

      Multi-Branch Decisions

      • The case statement
      • Menu driven applications
      • Exercise: Developing and writing a menu system


      • What is a function?
      • Syntax
      • Examples
      • Exercise: Add a function to a script

      Interrupt Handling

      • Interrupt signals
      • Trapping interrupts
      • Exercise: Adding traps to the menu script

      Additional Features and Facilities

      • The exec commands
      • The includes notation
      • More about loops
      • Arrays
      • Here Documents
      • Exercise: Create a here script

      Datastage Certification Training

      The Datastage Certification Program can give you a distinct advantage. An Datastage Certification demonstrates that you have a solid understanding of a job role and the Datastage products used in that role. Becoming an Datastage Certified Professional can help raise your visibility and increase your access to the industry's most challenging opportunities.

      Datawarehouse ETL Tool Training Courses in Chennai

      Datastage Training Course Highlights:

      • 1) Two days free trial - If candidate likes this course, these days are adjusted in his actual schedule.
      • 2) Live Project Exposure of Fortune companies.
      • 3) Training by Subject Matter experts from CMM Level 5 companies
      • 4) Running in two major financial cities of India – Chennai and Mumbai
      • 5) Worldwide online training of Datawarehouse and corporate classes at affordable fees.
      • 6) Our basic course worth more than the advanced course of other institutes/freelancers.
      • 7) Free Interview preparations.
      • 8) 100% free assistance for Datastage certifications.
      • 9) 100 % guarantee in succeeding the certification at affordable fees.
      • 10) Also provide online training to students of foreign countries.
      • Learn Datastage Training from the Best Datawarehouse Institute in Chennai

      Datastage Training Locations in Chennai

      Greens Technology
      15 First Street Padmanabha Nagar, Adyar, Chennai
      Tel: +91- 89399 15577
            +91- 89399 25577

      Datastage Training Reviews

      Greens Technology Reviews given by our students already completed the training with us. Please give your feedback as well if you are a student.