DataStage Training

DataStage Training consists of advanced concepts of data modelling, data stage administrator, director and designer. DataStage is an ETL tool and it uses a graphical notation to construct data integration solutions and is available in various Editions.

DataStage Training Curriculum

Data Warehouse Basics

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
Data Stage Administrator

Data stage project Administration
Editing projects and Adding Projects
Deleting projects and Cleansing up project files
Environmental Variables
Environment management
Auto purging
Runtime Column Propagation(RCP)
Add checkpoints for sequencer
NLS configuration
Generated OSH (Orchestra Engine)
System formats like data timestamp
Projects protect – Version details
Data Stage 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
Data Stage Designer

Introduction to Data stage Designer
Importance of Parallelism
Pipeline Parallelism
Partition Parallelism
Partitioning and collecting (In depth coverage of partitioning and collective techniques)
Symmetric Multi Processing (SMP) and Massively Parallel Processing (MPP)
Introduction to Configuration file
Editing a Configuration file
Partition techniques
Data stage Repository Palette
Passive and Active stages
Job design overview
Designer work area
Annotations and Creating jobs and Importing flat file definitions
Managing the Metadata environment
Dataset management
Deletion of Dataset
Routines
Working With Parallel Job Stages

Database Stages, Oracle, ODBC, Dynamic RDBMS
File Stages, Sequential file, Dataset
File set, Lookup file set
Processing Stages, Copy, Filter, Funnel
Sort, Remove duplicate, Aggregator, Switch
Pivot stage, Lookup, Join, Merge
Difference between look up, join and merge
Change capture, External Filter, Surrogate key generator
Transformer, Real time scenarios using different Processing Stages – Implementing different logics using Transformer
Debug Stages, Head, Tail, Peek
Column generator, Row generator
Write Range Map Stage, Real Time Stages, XML input
XML output, Local and Shared containers
Routines creation, Extensive usage of Job parameters
Parameter Sets, Environmental variables in jobs
Introduction to predefined Environmental variables creating user defined Environmental variables
Implementing the same in parallel jobs
Advanced Stages In Parallel Jobs

Explanation of Type1 and Type 2 processes
Implementation of Type1 and Type2 logics using Change Capture stage and SCD Stage
Range Look process
Surrogate key generator stage
FTP stage
Job performance analysis
Resource estimation
Performance tuning
Job Sequencers

Arrange job activities in Sequencer
Triggers in Sequencer
Restablity
Recoverability
Notification activity
Terminator activity
Wait for file activity
Start Loop activity
Execute Command activity
Nested Condition activity
Exception handling activity
End Loop activity
Adding Checkpoints
Jobs used in different real time scenarios
Explanation of Sequence Job stages through different Jobs
IBM Information Server Administration Guide

IBM Web 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
Additional Features
Performance Tuning of Parallel Jobs
Data stage Installation process and setup
Project Explanation.

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